Beyond Mobile Apps: A Survey of Technologies for Mental Well-being
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paper_title: Stress and Heart Rate Variability: A Meta-Analysis and Review of the Literature

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Objective-Physical or mental imbalance caused by harmful stimuli can induce stress to maintain homeostasis. During chronic stress, the sympathetic...
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paper_title: Experience sampling research in psychopathology: opening the black box of daily life

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A growing body of research suggests that momentary assessment technologies that sample experiences in the context of daily life constitute a useful and productive approach in the study of behavioural phenotypes and a powerful addition to mainstream cross-sectional research paradigms. Momentary assessment strategies for psychopathology are described, together with a comprehensive review of research findings illustrating the added value of daily life research for the study of (1) phenomenology, (2) aetiology, (3) psychological models, (4) biological mechanisms, (5) treatment and (6) gene-environment interactions in psychopathology. Overall, this review shows that variability over time and dynamic patterns of reactivity to the environment are essential features of psychopathological experiences that need to be captured for a better understanding of their phenomenology and underlying mechanisms. The Experience Sampling Method (ESM) allows us to capture the film rather than a snapshot of daily life reality of patients, fuelling new research into the gene-environment-experience interplay underlying psychopathology and its treatment.
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paper_title: A Stress Sensor Based on Galvanic Skin Response (GSR) Controlled by ZigBee

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Sometimes, one needs to control different emotional situations which can lead the person suffering them to dangerous situations, in both the medium and short term. There are studies which indicate that stress increases the risk of cardiac problems. In this study we have designed and built a stress sensor based on Galvanic Skin Response (GSR), and controlled by ZigBee. In order to check the device's performance, we have used 16 adults (eight women and eight men) who completed different tests requiring a certain degree of effort, such as mathematical operations or breathing deeply. On completion, we appreciated that GSR is able to detect the different states of each user with a success rate of 76.56%. In the future, we plan to create an algorithm which is able to differentiate between each state.
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paper_title: NeuroPlace: Categorizing urban places according to mental states

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Urban spaces have a great impact on how people’s emotion and behaviour. There are number of factors that impact our brain responses to a space. This paper presents a novel urban place recommendation approach, that is based on modelling in-situ EEG data. The research investigations leverages on newly affordable Electroencephalogram (EEG) headsets, which has the capability to sense mental states such as meditation and attention levels. These emerging devices have been utilized in understanding how human brains are affected by the surrounding built environments and natural spaces. In this paper, mobile EEG headsets have been used to detect mental states at different types of urban places. By analysing and modelling brain activity data, we were able to classify three different places according to the mental state signature of the users, and create an association map to guide and recommend people to therapeutic places that lessen brain fatigue and increase mental rejuvenation. Our mental states classifier has achieved accuracy of (%90.8). NeuroPlace breaks new ground not only as a mobile ubiquitous brain monitoring system for urban computing, but also as a system that can advise urban planners on the impact of specific urban planning policies and structures. We present and discuss the challenges in making our initial prototype more practical, robust, and reliable as part of our on-going research. In addition, we present some enabling applications using the proposed architecture.
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paper_title: Ecological Momentary Assessment

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Assessment in clinical psychology typically relies on global retrospective self-reports collected at research or clinic visits, which are limited by recall bias and are not well suited to address how behavior changes over time and across contexts. Ecological momentary assessment (EMA) involves repeated sampling of subjects’ current behaviors and experiences in real time, in subjects’ natural environments. EMA aims to minimize recall bias, maximize ecological validity, and allow study of microprocesses that influence behavior in real-world contexts. EMA studies assess particular events in subjects’ lives or assess subjects at periodic intervals, often by random time sampling, using technologies ranging from written diaries and telephones to electronic diaries and physiological sensors. We discuss the rationale for EMA, EMA designs, methodological and practical issues, and comparisons of EMA and recall data. EMA holds unique promise to advance the science and practice of clinical psychology by shedding ligh...
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paper_title: Electrodermal Activity Sensor for Classification of Calm/Distress Condition

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This article introduces a new and unobtrusive wearable monitoring device based on electrodermal activity (EDA) to be used in health-related computing systems. This paper introduces the description of the wearable device capable of acquiring the EDA of a subject in order to detect his/her calm/distress condition from the acquired physiological signals. The lightweight wearable device is placed in the wrist of the subject to allow continuous physiological measurements. With the aim of validating the correct operation of the wearable EDA device, pictures from the International Affective Picture System are used in a control experiment involving fifty participants. The collected signals are processed, features are extracted and a statistical analysis is performed on the calm/distress condition classification. The results show that the wearable device solely based on EDA signal processing reports around 89% accuracy when distinguishing calm condition from distress condition.
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paper_title: Self-Monitoring of Dietary Intake by Young Women: Online Food Records Completed on Computer or Smartphone Are as Accurate as Paper-Based Food Records but More Acceptable

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Adherence and accuracy of self-monitoring of dietary intake influences success in weight management interventions. Information technologies such as computers and smartphones have the potential to improve adherence and accuracy by reducing the burden associated with monitoring dietary intake using traditional paper-based food records. We evaluated the acceptability and accuracy of three different 7-day food record methods (online accessed via computer, online accessed via smartphone, and paper-based). Young women (N=18; aged 23.4±2.9 years; body mass index 24.0±2.2) completed the three 7-day food records in random order with 7-day washout periods between each method. Total energy expenditure (TEE) was derived from resting energy expenditure (REE) measured by indirect calorimetry and physical activity level (PAL) derived from accelerometers (TEE=REE×PAL). Accuracy of the three methods was assessed by calculating absolute (energy intake [EI]-TEE) and percentage difference (EI/TEE×100) between self-reported EI and TEE. Acceptability was assessed via questionnaire. Mean±standard deviation TEE was 2,185±302 kcal/day and EI was 1,729±249 kcal/day, 1,675±287kcal/day, and 1,682±352 kcal/day for computer, smartphone, and paper records, respectively. There were no significant differences between absolute and percentage differences between EI and TEE for the three methods: computer, -510±389 kcal/day (78%); smartphone, -456±372 kcal/day (80%); and paper, -503±513 kcal/day (79%). Half of participants (n=9) preferred computer recording, 44.4% preferred smartphone, and 5.6% preferred paper-based records. Most participants (89%) least preferred the paper-based record. Because online food records completed on either computer or smartphone were as accurate as paper-based records but more acceptable to young women, they should be considered when self-monitoring of intake is recommended to young women.
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paper_title: Experience sampling methodology in mental health research: new insights and technical developments.

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In the mental health field, there is a growing awareness that the study of psychiatric symptoms in the context of everyday life, using experience sampling methodology (ESM), may provide a powerful and necessary addition to more conventional research approaches. ESM, a structured self-report diary technique, allows the investigation of experiences within, and in interaction with, the real-world context. This paper provides an overview of how zooming in on the micro-level of experience and behaviour using ESM adds new insights and additional perspectives to standard approaches. More specifically, it discusses how ESM: a) contributes to a deeper understanding of psychopathological phenomena, b) allows to capture variability over time, c) aids in identifying internal and situational determinants of variability in symptomatology, and d) enables a thorough investigation of the interaction between the person and his/her environment and of real-life social interactions. Next to improving assessment of psychopathology and its underlying mechanisms, ESM contributes to advancing and changing clinical practice by allowing a more fine-grained evaluation of treatment effects as well as by providing the opportunity for extending treatment beyond the clinical setting into real life with the development of ecological momentary interventions. Furthermore, this paper provides an overview of the technical details of setting up an ESM study in terms of design, questionnaire development and statistical approaches. Overall, although a number of considerations and challenges remain, ESM offers one of the best opportunities for personalized medicine in psychiatry, from both a research and a clinical perspective.
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paper_title: Insomnia, Nightmares, and Chronotype as Markers of Risk for Severe Mental Illness: Results from a Student Population.

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To group participants according to markers of risk for severe mental illness based on subsyndromal symptoms reported in early adulthood and evaluate attributes of sleep across these risk categories.An online survey of sleep and psychiatric symptomatology (The Oxford Sleep Survey) was administered to students at one United Kingdom university. 1403 students (undergraduate and postgraduate) completed the survey. The median age was 21 (interquartile range = 20-23) and 55.60% were female. The cross-sectional data were used to cluster participants based on dimensional measures of psychiatric symptoms (hallucinations, paranoia, depression, anxiety, and (hypo)mania). High, medium, and low symptom groups were compared across sleep parameters: insomnia symptoms, nightmares, chronotype, and social jet lag.Insomnia symptoms, nightmares frequency, and nightmare-related distress increased in a dose-response manner with higher reported subsyndromal psychiatric symptoms (low, medium, and high). The high-risk group exhibited a later chronotype (mid sleep point for free days) than the medium- or low-risk group. The majority of participants (71.7%) in the high-risk group screened positive for insomnia and the median nightmare frequency was two per 14 days (moderately severe pathology).Insomnia, nightmares, and circadian phase delay are associated with increased subsyndromal psychiatric symptoms in young people. Each is a treatable sleep disorder and might be a target for early intervention to modify the subsequent progression of psychiatric disorder.
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paper_title: A supermarket stress map

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People choose their favorite supermarket based on many factors including how the place is designed and organized. Customers generally perceive their shopping experience in an aggregated way, which leaves little room for supermarkets managers to understand what triggers negative feelings. Moreover, adapting the supermarket design to clients' needs is crucial to retain current customers. Accommodating facilities make shoppers feel pleased. In this paper we show the design and initial deployment of a pervasive system that registers location-stamped stress levels of supermarket customers during their shopping. The system aims to discover stress hot spots in a supermarket, which will help managers locate and solve design and store management deficiencies.
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paper_title: Skin temperature reveals the intensity of acute stress

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Acute stress triggers peripheral vasoconstriction, causing a rapid, short-term drop in skin temperature in homeotherms. We tested, for the first time, whether this response has the potential to quantify stress, by exhibiting proportionality with stressor intensity. We used established behavioural and hormonal markers: activity level and corticosterone level, to validate a mild and more severe form of an acute restraint stressor in hens (Gallus gallus domesticus). We then used infrared thermography (IRT) to non-invasively collect continuous temperature measurements following exposure to these two intensities of acute handling stress. In the comb and wattle, two skin regions with a known thermoregulatory role, stressor intensity predicted the extent of initial skin cooling, and also the occurrence of a more delayed skin warming, providing two opportunities to quantify stress. With the present, cost-effective availability of IRT technology, this non-invasive and continuous method of stress assessment in unrestrained animals has the potential to become common practice in pure and applied research.
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paper_title: Promoting and Protecting Youth Mental Health through Evidence-Based Prevention and Treatment.

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For decades, empirically tested youth interventions have prevented dysfunction by addressing risk and ameliorated dysfunction through treatment. The authors propose linking prevention and treatment within an integrated model. The model suggests a research agenda: Identify effective programs for a broadened array of problems and disorders, examine ethnicity and culture in relation to intervention adoption and impact, clarify conditions under which programs do and do not work, identify change mechanisms that account for effects, test interventions in real-world contexts, and make tested interventions accessible and effective in community and practice settings. Connecting the science and practice of prevention and treatment will be good for science, for practice, and for children, adolescents, and their families.
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paper_title: What is the impact of mental health-related stigma on help-seeking?: A systematic review of quantitative and qualitative studies

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Background Individuals often avoid or delay seeking professional help for mental health problems. Stigma may be a key deterrent to help-seeking but this has not been reviewed systematically. Our systematic review addressed the overarching question: What is the impact of mental health-related stigma on help-seeking for mental health problems? Subquestions were: ( a ) What is the size and direction of any association between stigma and help-seeking? ( b ) To what extent is stigma identified as a barrier to help-seeking? ( c ) What processes underlie the relationship between stigma and help-seeking? ( d ) Are there population groups for which stigma disproportionately deters help-seeking? Method Five electronic databases were searched from 1980 to 2011 and references of reviews checked. A meta-synthesis of quantitative and qualitative studies, comprising three parallel narrative syntheses and subgroup analyses, was conducted. Results The review identified 144 studies with 90 189 participants meeting inclusion criteria. The median association between stigma and help-seeking was d = − 0.27, with internalized and treatment stigma being most often associated with reduced help-seeking. Stigma was the fourth highest ranked barrier to help-seeking, with disclosure concerns the most commonly reported stigma barrier. A detailed conceptual model was derived that describes the processes contributing to, and counteracting, the deterrent effect of stigma on help-seeking. Ethnic minorities, youth, men and those in military and health professions were disproportionately deterred by stigma. Conclusions Stigma has a small- to moderate-sized negative effect on help-seeking. Review findings can be used to help inform the design of interventions to increase help-seeking.
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paper_title: Reliability and Validity of Daily Self-Monitoring by Smartphone Application for Health-Related Quality-of-Life, Antiretroviral Adherence, Substance Use, and Sexual Behaviors Among People Living with HIV

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This paper examines inter-method reliability and validity of daily self-reports by smartphone application compared to 14-day recall web-surveys repeated over 6 weeks with people living with HIV (PLH). A participatory sensing framework guided participant-centered design prioritizing external validity of methods for potential applications in both research and self-management interventions. Inter-method reliability correlations were consistent with prior research for physical and mental health quality-of-life (r = 0.26–0.61), antiretroviral adherence (r = 0.70–0.73), and substance use (r = 0.65–0.92) but not for detailed sexual encounter surveys (r = 0.15–0.61). Concordant and discordant pairwise comparisons show potential trends in reporting biases, for example, lower recall reports of unprotected sex or alcohol use, and rounding up errors for frequent events. Event-based reporting likely compensated for modest response rates to daily time-based prompts, particularly for sexual and drug use behaviors that may not occur daily. Recommendations are discussed for future continuous assessment designs and analyses.
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paper_title: Patient non-compliance with paper diaries

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Doctors often ask patients to recall recent health experiences, such as pain, fatigue, and quality of life.1 Research has shown, however, that recall is unreliable and rife with inaccuracies and biases.2 Recognition of recall's shortcomings has led to the use of diaries, which are intended to capture experiences close to the time of occurrence, thus limiting recall bias and producing more accurate data.3 ::: ::: The rationale for using diaries would be undermined if patients failed to complete diaries according to protocol. In this study we used a newly developed paper diary that could objectively record when patients made diary entries in order to compare patients' reported and actual compliance with diary keeping. For comparison, we also used an electronic diary designed to enhance compliance in order to assess what compliance rates might be achieved. ::: ::: We recruited 80 adults with chronic pain (pain for ≥3 hours a day and rated ≥4 on a 10 point scale) and assigned 40 to keeping a paper …
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paper_title: Mental Health Consumers' Experience of Stigma

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The extent to which mental health consumers encounter stigma in their daily lives is a matter of substantial importance for their recovery and quality of life. This article summarizes the results of a nationwide survey of 1,301 mental health consumers concerning their experience of stigma and discrimination. Survey results and followup interviews with 100 respondents revealed experience of stigma from a variety of sources, including communities, families, churches, coworkers, and mental health caregivers. The majority of respondents tended to try to conceal their disorders and worried a great deal that others would find out about their psychiatric status and treat them unfavorably. They reported discouragement, hurt, anger, and lowered self-esteem as results of their experiences, and they urged public education as a means for reducing stigma. Some reported that involvement in advocacy and speaking out when stigma and discrimination were encountered helped them to cope with stigma. Limitations to generalization of results include the self-selection, relatively high functioning of participants, and respondent connections to a specific advocacy organization-the National Alliance for the Mentally Ill.
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paper_title: Mobile mental health: Review of the emerging field and proof of concept study

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BackgroundThe ubiquitous nature of mobile phones and their increasing functionality make them an ideal medium for the delivery of large-scale public health information and interventions. While mobile phones have been used to this end in behavioural and physical health settings, their role in monitoring and managing mental health is in its infancy.AimsThe purpose of this paper is (1) to provide an overview of the field of mobile mental health and (2) by way of illustration, describe an initial proof of concept study carried out to assess the potential utility and effectiveness of a newly developed mobile phone and web-based program in the management of mild-to-moderate stress, anxiety and depression.MethodsOver 6 weeks, participants were given access to “myCompass”: an interactive self-help program, which includes real-time self-monitoring with short message service prompts and brief online modules grounded in cognitive behavioural therapy.ResultsPreliminary analyses found that participants' symptoms of st...
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paper_title: A Randomised Controlled Trial of a Brief Online Mindfulness-Based Intervention in a Non-clinical Population: Replication and Extension

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Building on previous research, this study compared the effects of two brief, online mindfulness-based interventions (MBIs; with and without formal meditation practice) and a no intervention control group in a non-clinical sample. One hundred and fifty-five university staff and students were randomly allocated to a 2-week, self-guided, online MBI with or without mindfulness meditation practice, or a wait list control. Measures of mindfulness, perceived stress, perseverative thinking and anxiety/depression symptoms within were administered before and after the intervention period. Intention to treat analysis identified significant differences between groups on change over time for all measured outcomes. Participation in the MBIs was associated with significant improvements in all measured domains (all ps < 0.05), with effect sizes in the small to medium range (0.25 to 0.37, 95% CIs 0.11 to 0.56). No significant changes on these measures were found for the control group. Change in perseverative thinking was found to mediate the relationship between condition and improvement on perceived stress and anxiety/depression symptom outcomes. Contrary to our hypotheses, no differences between the intervention conditions were found. Limitations of the study included reliance on self-report data, a relatively high attrition rate and absence of a longer-term follow-up. This study provides evidence in support of the feasibility and effectiveness of brief, self-guided MBIs in a non-clinical population and suggests that reduced perseverative thinking may be a mechanism of change. Our findings provide preliminary evidence for the effectiveness of a mindfulness psychoeducation condition, without an invitation to formal mindfulness meditation practice. Further research is needed to confirm and better understand these results and to test the potential of such interventions.
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paper_title: Self-Help Stress Management Training Through Mobile Phones: An Experience With Oncology Nurses

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Although all professions are susceptible to work-related stress, the nursing profession has been identifiedas particularly stressful; indeed, oncology nursing is often described as being among the most stressfulspecialty areas. The current study tested the short-term effects of an innovative self-help stress manage-ment training for oncology nurses supported by mobile tools. The sample included 30 female oncologynurses with permanent status employed in six oncology hospitals in Milan, Italy. The stress inoculationtraining (SIT) methodology served as the basis of the training, with the innovative challenge being theuse of mobile phones to support the stress management experience. To test the efficacy of the protocol,the study used a between-subjects design, comparing the experimental condition (SIT through mobilephones) with a control group (neutral video through mobile phones). The findings indicated psycholog-ical improvement of the experimental group in terms of anxiety state, anxiety trait reduction, and copingskills acquisition. This paper discusses implications for the implementation of this protocol in severalcontexts.
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paper_title: Artificial Intelligence-Assisted Online Social Therapy for Youth Mental Health

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Introduction: Benefits from mental health early interventions may not be sustained over time, and longer-term intervention programs may be required to maintain early clinical gains. However, due to the high intensity of face-to-face early intervention treatments, this may not be feasible. Adjunctive internet-based interventions specifically designed for youth may provide a cost-effective and engaging alternative to prevent loss of intervention benefits. However, until now online interventions have relied on human moderators to deliver therapeutic content. More sophisticated models responsive to user data are critical to inform tailored online therapy. Thus, integration of user experience with a sophisticated and cutting-edge technology to deliver content is necessary to redefine online interventions in youth mental health. This paper discusses the development of the moderated online social therapy (MOST) web application, which provides an interactive social media-based platform for recovery in mental health. We provide an overview of the system's main features and discus our current work regarding the incorporation of advanced computational and artificial intelligence methods to enhance user engagement and improve the discovery and delivery of therapy content. Methods: Our case study is the ongoing Horyzons site (5-year randomised controlled trial for youth recovering from early psychosis), which is powered by MOST. We outline the motivation underlying the project and the web application’s foundational features and interface. We discuss system innovations, including the incorporation of pertinent usage patterns as well as identifying certain limitations of the system. This leads to our current motivations and focus on using computational and artificial intelligence methods to enhance user engagement, and to further improve the system with novel mechanisms for the delivery of therapy content to users. In particular, we cover our usage of natural language analysis and chatbot technologies as strategies to tailor interventions and scale up the system. Conclusions: To date, the innovative MOST system has demonstrated viability in a series of clinical research trials. Given the data-driven opportunities afforded by the software system, observed usage patterns and the aim to deploy it on a greater scale, an important next step in its evolution is the incorporation of advanced and automated content delivery mechanisms.
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paper_title: A Pilot Study of the DBT Coach: An Interactive Mobile Phone Application for Individuals with Borderline Personality Disorder and Substance Use Disorder

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Abstract Dialectical behavior therapy (DBT) has received strong empirical support and is practiced widely as a treatment for borderline personality disorder (BPD) and BPD with comorbid substance use disorders (BPD-SUD). Therapeutic success in DBT requires that individuals generalize newly acquired skills to their natural environment. However, there have been only a limited number of options available to achieve this end. The primary goal of this research was to develop and test the feasibility of the DBT Coach, a software application for a smartphone, designed specifically to enhance generalization of a specific DBT skill (opposite action) among individuals with BPD-SUD. We conducted a quasiexperimental study in which 22 individuals who were enrolled in DBT treatment programs received a smartphone with the DBT Coach for 10 to 14 days and were instructed to use it as needed. Participants used the DBT Coach an average of nearly 15 times and gave high ratings of helpfulness and usability. Results indicate that both emotion intensity and urges to use substances significantly decreased within each coaching session. Furthermore, over the trial period, participants reported a decrease in depression and general distress. Mobile technology offering in vivo skills coaching may be a useful tool for reducing urges to use substances and engage in other maladaptive behavior by directly teaching and coaching in alternative, adaptive coping behavior.
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paper_title: Smartphone Ownership and Interest in Mobile Applications to Monitor Symptoms of Mental Health Conditions

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Background: Patient retrospective recollection is a mainstay of assessing symptoms in mental health and psychiatry. However, evidence suggests that these retrospective recollections may not be as accurate as data collection though the experience sampling method (ESM), which captures patient data in “real time” and “real life.” However, the difficulties in practical implementation of ESM data collection have limited its impact in psychiatry and mental health. Smartphones with the capability to run mobile applications may offer a novel method of collecting ESM data that may represent a practical and feasible tool for mental health and psychiatry. Objective: This paper aims to provide data on psychiatric patients’ prevalence of smartphone ownership, patterns of use, and interest in utilizing mobile applications to monitor their mental health conditions. Methods: One hundred psychiatric outpatients at a large urban teaching hospital completed a paper-and-pencil survey regarding smartphone ownership, use, and interest in utilizing mobile applications to monitor their mental health condition. Results: Ninety-seven percent of patients reported owning a phone and 72% reported that their phone was a smartphone. Patients in all age groups indicated greater than 50% interest in using a mobile application on a daily basis to monitor their mental health condition. Conclusions: Smartphone and mobile applications represent a practical opportunity to explore new modalities of monitoring, treatment, and research of psychiatric and mental health conditions. [JMIR Mhealth Uhealth 2014;2(1):e2]
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paper_title: Daily longitudinal self-monitoring of mood variability in bipolar disorder and borderline personality disorder

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Abstract Background Traditionally, assessment of psychiatric symptoms has been relying on their retrospective report to a trained interviewer. The emergence of smartphones facilitates passive sensor-based monitoring and active real-time monitoring through time-stamped prompts; however there are few validated self-report measures designed for this purpose. Methods We introduce a novel, compact questionnaire, Mood Zoom (MZ), embedded in a customised smart-phone application. MZ asks participants to rate anxiety, elation, sadness, anger, irritability and energy on a 7-point Likert scale. For comparison, we used four standard clinical questionnaires administered to participants weekly to quantify mania (ASRM), depression (QIDS), anxiety (GAD-7), and quality of life (EQ-5D). We monitored 48 Bipolar Disorder (BD), 31 Borderline Personality Disorders (BPD) and 51 Healthy control (HC) participants to study longitudinal (median±iqr: 313±194 days) variation and differences of mood traits by exploring the data using diverse time-series tools. Results MZ correlated well ( | R | > 0 . 5 , p 0.0001 ) with QIDS, GAD-7, and EQ-5D. We found statistically strong ( | R | > 0 . 3 , p 0.0001 ) differences in variability in all questionnaires for the three cohorts. Compared to HC, BD and BPD participants exhibit different trends and variability, and on average had higher self-reported scores in mania, depression, and anxiety, and lower quality of life. In particular, analysis of MZ variability can differentiate BD and BPD which was not hitherto possible using the weekly questionnaires. Limitations All reported scores rely on self-assessment; there is a lack of ongoing clinical assessment by experts to validate the findings. Conclusions MZ could be used for efficient, long-term, effective daily monitoring of mood instability in clinical psychiatric practice.
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paper_title: Designing and evaluating mobile self-reporting techniques: crowdsourcing for citizen science

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In recent years, mobile phone technology has taken tremendous leaps and bounds to enable all types of sensing applications and interaction methods, including mobile journaling and self-reporting to add metadata and to label sensor data streams. Mobile self-report techniques are used to record user ratings of their experiences during structured studies, instead of traditional paper-based surveys. These techniques can be timely and convenient when data are collected "in the wild". This paper proposes three new viable methods for mobile self-reporting projects and in real-life settings such as recording weather information or urban noise mapping. These techniques are Volume Buttons control, NFC-on-Body, and NFC-on-Wall. This work also provides an experimental and comparative analysis of various self-report techniques regarding user preferences and submission rates based on a series of user experiments. The statistical analysis of our data showed that pressing screen buttons and screen touch allowed for higher labelling rates, while Volume Buttons proved to be more valuable when users engaged in other activities, e.g. while walking. Similarly, based on participants’ preferences, we found that NFC labelling was also an easy and intuitive technique when used in the context of self-reporting and place-tagging. Our hope is that by reviewing current self-reporting interfaces and user requirements, we will be able to enable new forms of self-reporting technologies that were not possible before.
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paper_title: Mindfulness and Compassion: An Examination of Mechanism and Scalability

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Emerging evidence suggests that meditation engenders prosocial behaviors meant to benefit others. However, the robustness, underlying mechanisms, and potential scalability of such effects remain open to question. The current experiment employed an ecologically valid situation that exposed participants to a person in visible pain. Following three-week, mobile-app based training courses in mindfulness meditation or cognitive skills (i.e., an active control condition), participants arrived at a lab individually to complete purported measures of cognitive ability. Upon entering a public waiting area outside the lab that contained three chairs, participants seated themselves in the last remaining unoccupied chair; confederates occupied the other two. As the participant sat and waited, a third confederate using crutches and a large walking boot entered the waiting area while displaying discomfort. Compassionate responding was assessed by whether participants gave up their seat to allow the uncomfortable confederate to sit, thereby relieving her pain. Participants’ levels of empathic accuracy was also assessed. As predicted, participants assigned to the mindfulness meditation condition gave up their seats more frequently than did those assigned to the active control group. In addition, empathic accuracy was not increased by mindfulness practice, suggesting that mindfulness-enhanced compassionate behavior does not stem from associated increases in the ability to decode the emotional experiences of others.
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paper_title: Mobile telephone apps in mental health practice: uses, opportunities and challenges

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Smartphones are used by patients and clinicians alike. Vast numbers of software applications (apps) run on smartphones and carry out useful functions. Clinician- and patient-oriented mental health apps have been developed. In this article, we provide an overview of apps that are relevant for mental health. We look at clinician-oriented apps that support assessment, diagnosis and treatment as well as patient-oriented apps that support education and self-management. We conclude by looking at the challenges that apps pose with a discussion of possible solutions.
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paper_title: A Conversational Agent for an Online Mental Health Intervention

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This study investigated suitability of chatbots for a mental health intervention, specifically alcohol drinking habits assessment. The target group was young adults 18–25 years, the highest consumers of alcohol per capita in Australia. A chatbot program was developed to perform a standard assessment of alcohol drinking habits (AUDIT-C, 3 items rated on 5-point scale) to determine the level of health risk. Additionally, the chatbot provided information and education on responsible alcohol use, giving recommendations and feedback post-assessment using a pre-populated database of factual response contents. Usability and user-satisfaction were determined by a cohort study of 17 volunteer participants. Overall, the trial indicated strong positive reception of the intervention by users.
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paper_title: NotiMind: Utilizing Responses to Smart Phone Notifications as Affective sensors

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Today’s mobile phone users are faced with large numbers of notifications on social media, ranging from new followers on Twitter and emails to messages received from WhatsApp and Facebook. These digital alerts continuously disrupt activities through instant calls for attention. This paper examines closely the way everyday users interact with notifications and their impact on users’ emotion. Fifty users were recruited to download our application NotiMind and use it over a five-week period. Users’ phones collected thousands of social and system notifications along with affect data collected via self-reported Positive and Negative Affect Schedule tests three times a day. Results showed a noticeable correlation between positive affective measures and keyboard activities. When large numbers of post and remove notifications occur, a corresponding increase in negative affective measures is detected. Our predictive model has achieved a good accuracy level using three different ”in the wild” classifiers (F-measure 74%–78% within-subject model, 72%–76% global model). Our findings show that it is possible to automatically predict when people are experiencing positive, neutral, or negative affective states based on interactions with notifications. We also show how our findings open the door to a wide range of applications in relation to emotion awareness on social and mobile communication.
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paper_title: Online-based Mindfulness Training Reduces Behavioral Markers of Mind Wandering

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It is estimated that people spend almost half their waking hours lost in stimulus-independent thought, or mind wandering, which in turn has been shown to negatively impact well-being. This has sparked a rise in the number of cognitive training platforms that aim to boost executive functioning, yet it is unclear whether mind wandering can be reduced through online training. The current study aimed to investigate whether behavioral markers of mind wandering can be reduced through two short-term online-based interventions: mindfulness meditation and brain training. Using a randomized controlled design, we assigned one group of participants to 30 days of mindfulness training (n = 54) and another to 30 days of brain training (n = 41). Mind wandering and dispositional mindfulness were assessed pre- and post-intervention via the Sustained Attention to Response Task (SART) and the Mindful Attention to Awareness Scale (MAAS), respectively. We found significant reductions in mind wandering and significant increases in dispositional mindfulness in the mindfulness training group but not the brain training group. A lack of any significant change in the brain training group may be driven by methodological limitations such as self-report bias. These results indicate that short online mindfulness-based interventions may be effective in reducing mind wandering.
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paper_title: Assessing Barriers to Care and Readiness for Cognitive Behavioral Therapy in Early Acute Care PTSD Interventions

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Cognitive Behavioral Therapy (CBT) interventions are efficacious in reducing posttraumatic stress disorder (PTSD) but are challenging to implement in acute care and other non-specialty mental health settings. This investigation identified barriers impacting CBT delivery through a content analysis of interventionist chart notes from an acute care PTSD prevention trial. Only 8.5% of all intervention patients were able to complete CBT. Lack of engagement, clinical and logistical barriers had the greatest impact on CBT entry. Treatment preferences and stigma only prevented entry when more primary barriers resolved. Patients with prior diagnosis of alcohol abuse or dependence were able to enter CBT after six months of sobriety. Based on the first trial, we developed a CBT readiness assessment tool. We implemented and evaluated the tool in a second early intervention trial. Lack of engagement emerged again as the primary impediment to CBT entry. Patients who were willing to enter CBT treatment but demonstrated ...
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paper_title: Ethical perspectives on recommending digital technology for patients with mental illness

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The digital revolution in medicine not only offers exciting new directions for the treatment of mental illness, but also presents challenges to patient privacy and security. Changes in medicine are part of the complex digital economy based on creating value from analysis of behavioral data acquired by the tracking of daily digital activities. Without an understanding of the digital economy, recommending the use of technology to patients with mental illness can inadvertently lead to harm. Behavioral data are sold in the secondary data market, combined with other data from many sources, and used in algorithms that automatically classify people. These classifications are used in commerce and government, may be discriminatory, and result in non-medical harm to patients with mental illness. There is also potential for medical harm related to poor quality online information, self-diagnosis and self-treatment, passive monitoring, and the use of unvalidated smartphone apps. The goal of this paper is to increase awareness and foster discussion of the new ethical issues. To maximize the potential of technology to help patients with mental illness, physicians need education about the digital economy, and patients need help understanding the appropriate use and limitations of online websites and smartphone apps.
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paper_title: A randomised active-controlled trial to examine the effects of an online mindfulness intervention on executive control, critical thinking and key thinking dispositions in a university student sample

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BACKGROUND ::: Arguments for including mindfulness instruction in higher education have included claims about the benefits of mindfulness practice for critical thinking. While there is theoretical support for this claim, empirical support is limited. The aim of this study was to test this claim by investigating the effects of an online mindfulness intervention on executive function, critical thinking skills and associated thinking dispositions. ::: ::: ::: METHOD ::: Participants recruited from a university were randomly allocated, following screening, to either a mindfulness meditation group or a sham meditation group. Both the researchers and the participants were blind to group allocation. The intervention content for both groups was delivered through the Headspace online application, an application which provides guided meditations to users. Both groups were requested to complete 30 guided mindfulness meditation sessions across a 6 week period. Primary outcome measures assessed mindfulness, executive functioning, critical thinking, actively open-minded thinking and need for cognition. Secondary outcome measures assessed wellbeing, positive and negative affect, and real-world outcomes. ::: ::: ::: RESULTS ::: In a series of full-information maximum likelihood analyses, significant increases in mindfulness dispositions and critical thinking scores were observed in both the mindfulness meditation and sham meditation groups. However, no significant effects of group allocation were observed for either primary or secondary measures. Furthermore, mediation analyses testing the indirect effect of group allocation through executive functioning performance did not reveal a significant result and moderation analyses showed that the effect of the intervention did not depend on baseline levels of the key thinking dispositions, actively open-minded thinking and need for cognition. ::: ::: ::: CONCLUSION ::: No evidence was found to suggest that engaging in guided mindfulness practice for 6 weeks using the online intervention method applied in this study improves critical thinking performance. While further research is warranted, claims regarding the benefits of mindfulness practice for critical thinking should be tempered in the meantime. ::: ::: ::: TRIAL REGISTRATION ::: The study was initially registered in the AEA Social Science Registry before the recruitment was initiated (RCT ID: AEARCTR-0000756; 14/11/2015) and retrospectively registered in the ISRCTN registry ( RCT ID: ISRCTN16588423 ) in line with requirements for publishing the study protocol.
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paper_title: An Empathy-Driven, Conversational Artificial Intelligence Agent (Wysa) for Digital Mental Well-Being: Real-World Data Evaluation Mixed-Methods Study

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BACKGROUND ::: A World Health Organization 2017 report stated that major depression affects almost 5% of the human population. Major depression is associated with impaired psychosocial functioning and reduced quality of life. Challenges such as shortage of mental health personnel, long waiting times, perceived stigma, and lower government spends pose barriers to the alleviation of mental health problems. Face-to-face psychotherapy alone provides only point-in-time support and cannot scale quickly enough to address this growing global public health challenge. Artificial intelligence (AI)-enabled, empathetic, and evidence-driven conversational mobile app technologies could play an active role in filling this gap by increasing adoption and enabling reach. Although such a technology can help manage these barriers, they should never replace time with a health care professional for more severe mental health problems. However, app technologies could act as a supplementary or intermediate support system. Mobile mental well-being apps need to uphold privacy and foster both short- and long-term positive outcomes. ::: ::: ::: OBJECTIVE ::: This study aimed to present a preliminary real-world data evaluation of the effectiveness and engagement levels of an AI-enabled, empathetic, text-based conversational mobile mental well-being app, Wysa, on users with self-reported symptoms of depression. ::: ::: ::: METHODS ::: In the study, a group of anonymous global users were observed who voluntarily installed the Wysa app, engaged in text-based messaging, and self-reported symptoms of depression using the Patient Health Questionnaire-9. On the basis of the extent of app usage on and between 2 consecutive screening time points, 2 distinct groups of users (high users and low users) emerged. The study used mixed-methods approach to evaluate the impact and engagement levels among these users. The quantitative analysis measured the app impact by comparing the average improvement in symptoms of depression between high and low users. The qualitative analysis measured the app engagement and experience by analyzing in-app user feedback and evaluated the performance of a machine learning classifier to detect user objections during conversations. ::: ::: ::: RESULTS ::: The average mood improvement (ie, difference in pre- and post-self-reported depression scores) between the groups (ie, high vs low users; n=108 and n=21, respectively) revealed that the high users group had significantly higher average improvement (mean 5.84 [SD 6.66]) compared with the low users group (mean 3.52 [SD 6.15]); Mann-Whitney P=.03 and with a moderate effect size of 0.63. Moreover, 67.7% of user-provided feedback responses found the app experience helpful and encouraging. ::: ::: ::: CONCLUSIONS ::: The real-world data evaluation findings on the effectiveness and engagement levels of Wysa app on users with self-reported symptoms of depression show promise. However, further work is required to validate these initial findings in much larger samples and across longer periods.
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paper_title: Patient non-compliance with paper diaries

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Doctors often ask patients to recall recent health experiences, such as pain, fatigue, and quality of life.1 Research has shown, however, that recall is unreliable and rife with inaccuracies and biases.2 Recognition of recall's shortcomings has led to the use of diaries, which are intended to capture experiences close to the time of occurrence, thus limiting recall bias and producing more accurate data.3 ::: ::: The rationale for using diaries would be undermined if patients failed to complete diaries according to protocol. In this study we used a newly developed paper diary that could objectively record when patients made diary entries in order to compare patients' reported and actual compliance with diary keeping. For comparison, we also used an electronic diary designed to enhance compliance in order to assess what compliance rates might be achieved. ::: ::: We recruited 80 adults with chronic pain (pain for ≥3 hours a day and rated ≥4 on a 10 point scale) and assigned 40 to keeping a paper …
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paper_title: Development and usability testing of FOCUS: A smartphone system for self-management of schizophrenia.

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Objective: Mobile Health (mHealth) approaches can support the rehabilitation of individuals with psychiatric conditions. In the current article, we describe the development of a smartphone illness self-management system for people with schizophrenia. Methods: The research was conducted with consumers and practitioners at a community-based rehabilitation agency. Stage 1: 904 individuals with schizophrenia or schizoaffective disorder completed a survey reporting on their current use of mobile devices and interest in mHealth services. Eight practitioners completed a survey examining their attitudes and expectations from an mHealth intervention, and identified needs and potential obstacles. Stage 2: A multidisciplinary team incorporated consumer and practitioner input and employed design principles for the development of e-resources for people with schizophrenia to produce an mHealth intervention. Stage 3: 12 consumers participated in laboratory usability sessions. They performed tasks involved in operating the new system, and provided “think aloud” commentary and post-session usability ratings. Results: 570 (63%) of survey respondents reported owning a mobile device and many expressed interest in receiving mHealth services. Most practitioners believed that consumers could learn to use and would benefit from an mHealth intervention. In response, we developed a smartphone system that targets medication adherence, mood regulation, sleep, social functioning, and coping with symptoms. Usability testing revealed several design vulnerabilities, and the system was adapted to address consumer needs and preferences accordingly. Conclusions and Implications for Practice: Through a comprehensive development process, we produced an mHealth illness self-management intervention that is likely to be used successfully, and is ready for deployment and systemic evaluation in real-world conditions.
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paper_title: The chatbot feels you - a counseling service using emotional response generation

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Early study tries to use chatbot for counseling services. They changed drinking habit of who being consulted by leading them via intervene chatbot. However, the application did not concerned about psychiatric status through continuous conversation with user monitoring. Furthermore, they had no ethical judgment method that about the intervention of the chatbot. We argue that more reasonable and continuous emotion recognition will make better mental healthcare experiment. It will be more proper clinical psychiatric consolation in ethical view as well. This paper suggests a introduce a novel chatbot system for psychiatric counseling service. Our system understands content of conversation based on recent natural language processing (NLP) methods with emotion recognition. It senses emotional flow through the continuous observation of conversation. Also, we generate personalized counseling response from user input, to do this, we use additional constrains to generation model for the proper response generation which can detect conversational context, user emotion and expected reaction.
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paper_title: Meditation Inhibits Aggressive Responses to Provocations

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Meditation has been advocated as a mental practice designed to reduce suffering and increase virtuous behavior. Although it has been previously linked to altruistic acts, its ability to reduce aggression and related retributive behaviors remains open to question. Here, we report on an experiment in which participants were randomly assigned to a mindfulness meditation or active control condition 3 weeks prior to facing a real-time provocation known to evoke aggression. Participants’ capacities for executive control were also assessed subsequent to training. Results showed that 3 weeks of daily meditation practice substantially reduced aggressive behavior even in the absence of any enhanced executive control capabilities. These results suggest that meditation attenuates aggression through direct reductions in motives to cause harm to others.
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paper_title: Survey on Chatbot Design Techniques in Speech Conversation Systems

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Human-Computer Speech is gaining momentum as a technique of computer interaction. There has been a recent upsurge in speech based search engines and assistants such as Siri, Google Chrome and Cortana. Natural Language Processing (NLP) techniques such as NLTK for Python can be applied to analyse speech, and intelligent responses can be found by designing an engine to provide appropriate human like responses. This type of programme is called a Chatbot, which is the focus of this study. This paper presents a survey on the techniques used to design Chatbots and a comparison is made between different design techniques from nine carefully selected papers according to the main methods adopted. These papers are representative of the significant improvements in Chatbots in the last decade. The paper discusses the similarities and differences in the techniques and examines in particular the Loebner prize-winning Chatbots.
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paper_title: New technologies to manage exam anxiety.

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A Stress Inoculation Training-based protocol tested if multimedia audio-video content induced emotional changes and reduced exam anxiety in university students. Seventy-five participants took part in six experimental sessions consisting of viewing multimedia content and performing relaxation exercises. Participants were randomly assigned to five experimental groups: 1) audio and video narrative on mobile phone (UMTS); 2) audio and video narrative on DVD (DVD), 3) audio narrative on MP3 player (M3), 4) audio narrative on CD (CD), 5) control group (CTRL). Results showed that audio/video content induced a significant reduction in exam anxiety and an increase of relaxation in students, compared to the audio-only contents.
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paper_title: Service Use for Mental Health Problems: Findings from the 2007 National Survey of Mental Health and Wellbeing

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OBJECTIVES ::: To provide an overview of 12 month rates of service use for mental health problems and mental disorders by the general Australian adult population. ::: ::: ::: METHOD ::: Data came from the 2007 National Survey of Mental Health and Wellbeing (2007 NSMHWB), a nationally representative household survey of 8841 individuals aged between 16 and 85 years. ::: ::: ::: RESULTS ::: Overall, 11.9% of the general Australian adult population made use of any services for mental health problems in a 12 month period. Approximately one-third of people (34.9%) meeting criteria for a mental disorder did so. Female subjects with mental disorders were more likely to use services than male subjects (40.7% vs 27.5%). People in the youngest age group made relatively less use of services than older adults. Those with affective disorders were most likely to make use of services (58.6%), followed by those with anxiety (37.8%) and substance use disorders (24.0%), respectively. Mental health hospitalizations were less common than consultations with community-based providers (2.6%), whereas 34.6% consulted a community-based provider--particularly general practitioners (24.7%) and psychologists (13.2%). There was a clear dose-response effect between severity of disorders and rates of community-based service use: 63.5% of those with severe mental disorders used community-based services, compared with 40.2% and 17.7% of those with moderate and mild mental disorders, respectively. There was also a relationship between comorbidity of mental disorders and service use. ::: ::: ::: CONCLUSIONS ::: Rates of service use for mental health problems among those with mental disorders in Australia are less than optimal. Little international guidance is available regarding appropriate levels of treatment coverage and other comparable countries face similar difficulties. Further work is required to determine what an appropriate rate of service use is, and to set targets to reach that rate. Australia has demonstrated that concerted policy efforts can improve rates of service use. These efforts should be expanded.
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paper_title: Exploring affective technologies for the classroom with the subtle stone

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Constructive emotional experiences are strongly related to effective learning. Yet, it is challenging for teachers, researchers and students alike to understand the emotions experienced in the classroom setting. Advances in wireless and sensor technologies open up possibilities for better supporting emotions. However, little work has explored how affective technologies in the classroom might operate. This paper describes a study where 15 high school students used the Subtle Stone: a tangible technology designed to support students' active emotional communication in the classroom. We report on how the students used and experienced this technology, and the values they demonstrated through this use: flexibility, privacy, agency, voice and reflection. We conclude by examining future possibilities for affective technologies in the classroom.
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paper_title: In the mood: engaging teenagers in psychotherapy using mobile phones

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Mental illness is a significant and growing problem throughout the world. Many mental health problems have their root in childhood, and early intervention is recommended. Engaging young people in psychotherapeutic activities is challenging, and treatment adherence is often poor. This paper presents a series of studies carried out as part of the development of a mobile and online symptom tracking tool for adolescents with mental health problems. Teenagers use the system to record symptoms on their mobile phones and can view this information in a clinical setting with a therapist. We focus on a clinical pilot of the system with ten users in public mental health clinics. As well as indicating that the mobile diary tool can increase client adherence to therapeutic activities, the study yields insight into the factors influencing the success of the design and informs the design of other systems to be used as adjuncts to therapy.
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paper_title: A sprite in the dark: supporting conventional mental healthcare practices with a tangible device

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This paper describes the decision-making and motivation behind the development of Mood Sprite – an interactive tool for mental health. Our main aim with this project has been to design a device that compliments and augments the current practices employed by community mental health support groups. To this end, we worked directly with a community support group and a psychologist as co-designers.
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paper_title: Tangible Interaction in the Dentist Office

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This paper presents the design efforts involved in making a system for supporting haptic communication between dentist and patient during dental treatment. We describe Grasp Live, a haptic interaction technology consisting of a tangible stone-like object connected to a vibro-tactile feedback device.
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paper_title: Keppi: A Tangible User Interface for Self-Reporting Pain

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Motivated by the need to support those managing chronic pain, we report on the iterative design, development, and evaluation of Keppi, a novel pressure-based tangible user interface (TUI) for the self-report of pain intensity. In-lab studies with 28 participants found individuals were able to use Keppi to reliably report low, medium, and high pain as well as map squeeze pressure to pain level. Based on insights from these evaluations, we ultimately created a wearable version of Keppi with multiple form factors, including a necklace, bracelet, and keychain. Interviews indicated high receptivity to the wearable design, which satisfied additional user-identified needs (e.g., discreet and convenient) and highlighted key directions for the continued refinement of tangible devices for pain assessment.
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paper_title: Self-Monitoring of Emotions and Mood Using a Tangible Approach

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Nowadays Personal Informatics (PI) devices are used for sensing and saving personal data, everywhere and at any time, helping people improve their lives by highlighting areas of good and bad performances and providing a general awareness of different levels of conduct. However, not all these data are suitable to be automatically collected. This is especially true for emotions and mood. Moreover, users without experience in self-tracking may have a misperception of PI applications’ limits and potentialities. We believe that current PI tools are not designed with enough understanding of such users’ needs, desires, and problems they may encounter in their everyday lives. We designed and prototype the Mood TUI (Tangible User Interface), a PI tool that supports the self-reporting of mood data using a tangible interface. The platform is able to gather six different mood states and it was tested through several participatory design sessions in a secondary/high school. The solution proposed allows gathering mood values in an amusing, simple, and appealing way. Users appreciated the prototypes, suggesting several possible improvements as well as ideas on how to use the prototype in similar or totally different contexts, and giving us hints for future research.
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paper_title: Design and evaluation guidelines for mental health technologies

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It is increasingly recognised that technology has the potential to significantly improve access, engagement, effectiveness and affordability of treatment for mental health problems. The development of such technology has recently become the subject of Human-Computer Interaction research. As an emerging area with a unique set of constraints and design concerns, there is a need to establish guidelines which encapsulate the knowledge gained from existing development projects. We present an initial set of design guidelines extracted from the literature and from a series of development projects for software to support mental health interventions. The first group of guidelines pertain to the design process itself, addressing the limitations in access to clients in mental healthcare settings, and strategies for collaborative design with therapists. The second group considers major design factors in the development of these technologies, including therapeutic models, client factors, and privacy. The third group concerns conduct of the evaluation process, and the constraints on evaluating mental healthcare technologies. We motivate and explain these guidelines with reference to concrete design projects and problems.
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paper_title: A plethysmographic method for demonstrating the response specificity of the oral vascular bed

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In this paper, we describe a new method for measuring the oral plethysmogram, and we assess its sensitivity and specificity under differing psychological stimulation. Finger and palate pulse amplitudes and blood pressure were monitored while individuals (N = 13) performed several tasks: mental arithmetic, nausea imagery, fear imagery, and anger imagery. Pulse pressure, having a major effect on pulse amplitude, was partialed out in analyses. Palate pulse amplitude increased significantly in response to the degree to which the individual felt irritated, judged, nauseated, or angry. In contrast, finger pulse amplitude changed significantly only in the arithmetic task and, unlike the palate, showed a decreased amplitude with increased irritation and being judged. Results indicate that the oral plethysmogram can serve as a reliable measure of oral mucosal vasomotor reactivity and that it has a different pattern of response specificity than does the finger.
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paper_title: Advancements in Noncontact, Multiparameter Physiological Measurements Using a Webcam

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We present a simple, low-cost method for measuring multiple physiological parameters using a basic webcam. By applying independent component analysis on the color channels in video recordings, we extracted the blood volume pulse from the facial regions. Heart rate (HR), respiratory rate, and HR variability (HRV, an index for cardiac autonomic activity) were subsequently quantified and compared to corresponding measurements using Food and Drug Administration-approved sensors. High degrees of agreement were achieved between the measurements across all physiological parameters. This technology has significant potential for advancing personal health care and telemedicine.
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paper_title: Pulse wave velocity and the second derivative of the finger photoplethysmogram in treated hypertensive patients: their relationship and associating factors.

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Objectives To clarify the factors influencing two measures of arterial stiffness, pulse wave velocity (PWV) and the second derivative of the finger photoplethysmogram (SDPTG), and to evaluate their relationship in treated hypertensive subjects. Subjects and methods The subjects were 294 hypertensive patients aged 32-91 years (mean, 61.4 ± 10.5 years). After blood pressure (BP) was measured, carotid-femoral PWV and SDPTG were recorded with the subjects in the supine position, with the aid of an automatic device. For assessing SDPTG, we focused mainly on the ratios of the absolute value of the height of the early negative 'b' wave (B) and that of the late redecreasing 'd' wave (D) to that of the initial positive 'a' wave (A), namely the B : A and D : A ratios, and the aging index (AGI). Factors influencing PWV and SDPTG indices, and the relationship between PWV and SDPTG indices were evaluated by bivariate and multivariate analyses. Results According to multiple linear regression analysis, age, systolic blood pressure (SBP), and heart rate (HR) were variables independently and positively correlated with PWV. The age and BP were significantly and independently related to SDPTG indices; there were positive correlations with the D : A ratio and AGI, and negative correlations with the B : A ratio. HR was correlated negatively with the D : A ratio and AGI, and positively with the B : A ratio. According to multiple logistic regression analysis, adjusted-odds ratios of high PWV, low B : A ratio, high D : A ratio, and high AGI were significantly elevated among the elderly and among the subjects with uncontrolled BP. Although PWV and the SDPTG indices were associated with common factors, including age and BP, bivariate analysis revealed that they were only weakly correlated with each other. Conclusions The results suggest that in hypertensive patients, PWV and SDPTG provide different information about arterial properties at central and peripheral sites.
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paper_title: Heart rate variability: a review

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Heart rate variability (HRV) is a reliable reflection of the many physiological factors modulating the normal rhythm of the heart. In fact, they provide a powerful means of observing the interplay between the sympathetic and parasympathetic nervous systems. It shows that the structure generating the signal is not only simply linear, but also involves nonlinear contributions. Heart rate (HR) is a nonstationary signal; its variation may contain indicators of current disease, or warnings about impending cardiac diseases. The indicators may be present at all times or may occur at random-during certain intervals of the day. It is strenuous and time consuming to study and pinpoint abnormalities in voluminous data collected over several hours. Hence, HR variation analysis (instantaneous HR against time axis) has become a popular noninvasive tool for assessing the activities of the autonomic nervous system. Computer based analytical tools for in-depth study of data over daylong intervals can be very useful in diagnostics. Therefore, the HRV signal parameters, extracted and analyzed using computers, are highly useful in diagnostics. In this paper, we have discussed the various applications of HRV and different linear, frequency domain, wavelet domain, nonlinear techniques used for the analysis of the HRV.
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paper_title: Utilizing a Wristband Sensor to Measure the Stress Level for People with Dementia

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Stress is a common problem that affects most people with dementia and their caregivers. Stress symptoms for people with dementia are often measured by answering a checklist of questions by the clinical staff who work closely with the person with the dementia. This process requires a lot of effort with continuous observation of the person with dementia over the long term. This article investigates the effectiveness of using a straightforward method, based on a single wristband sensor to classify events of “Stressed” and “Not stressed” for people with dementia. The presented system calculates the stress level as an integer value from zero to five, providing clinical information of behavioral patterns to the clinical staff. Thirty staff members participated in this experiment, together with six residents suffering from dementia, from two nursing homes. The residents were equipped with the wristband sensor during the day, and the staff were writing observation notes during the experiment to serve as ground truth. Experimental evaluation showed relationships between staff observations and sensor analysis, while stress level thresholds adjusted to each individual can serve different scenarios.
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paper_title: Measurement of occupants' stress based on electroencephalograms (EEG) in twelve combined environments

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Abstract This study examines the effects of indoor environment on stress levels. Temperature, odor irritants, and sound were selected as environmental elements to be studied within the combined environment, and these were individually controlled in a total of 12 combined environments with established climate chambers. The occupants' stress was measured using paper-based tests, and the two combined environments that showed the most different stress aspects were determined based on their results. A second test was then performed using an electroencephalogram (EEG), and in this test, occupants' stress in the two combined environments were compared using an EEG analysis to generate a brain map showing occupants' stress states from a physiological perspective. Experimental results indicate that occupants' stress was maximized when they were exposed to a temperature of 30 °C, odor irritants (VOCs) and to road traffic noises. In this stressful environment, it is presumed that the differences in brain mapping of the relative high-beta wave in the temporal lobe can be useful when assessing participants' stress.
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paper_title: Remote measurement of cognitive stress via heart rate variability

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Remote detection of cognitive load has many powerful applications, such as measuring stress in the workplace. Cognitive tasks have an impact on breathing and heart rate variability (HRV). We show that changes in physiological parameters during cognitive stress can be captured remotely (at a distance of 3m) using a digital camera. A study (n=10) was conducted with participants at rest and under cognitive stress. A novel five band digital camera was used to capture videos of the face of the participant. Significantly higher normalized low frequency HRV components and breathing rates were measured in the stress condition when compared to the rest condition. Heart rates were not significantly different between the two conditions. We built a person-independent classifier to predict cognitive stress based on the remotely detected physiological parameters (heart rate, breathing rate and heart rate variability). The accuracy of the model was 85% (35% greater than chance).
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paper_title: AROMA: A Deep Multi-Task Learning Based Simple and Complex Human Activity Recognition Method Using Wearable Sensors

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Human activity recognition (HAR) is a promising research issue in ubiquitous and wearable computing. However, there are some problems existing in traditional methods: 1) They treat HAR as a single label classification task, and ignore the information from other related tasks, which is helpful for the original task. 2) They need to predesign features artificially, which are heuristic and not tightly related to HAR task. To address these problems, we propose AROMA (human activity recognition using deep multi-task learning). Human activities can be divided into simple and complex activities. They are closely linked. Simple and complex activity recognitions are two related tasks in AROMA. For simple activity recognition task, AROMA utilizes a convolutional neural network (CNN) to extract deep features, which are task dependent and non-handcrafted. For complex activity recognition task, AROMA applies a long short-term memory (LSTM) network to learn the temporal context of activity data. In addition, there is a shared structure between the two tasks, and the object functions of these two tasks are optimized jointly. We evaluate AROMA on two public datasets, and the experimental results show that AROMA is able to yield a competitive performance in both simple and complex activity recognitions.
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paper_title: Discriminating Stress From Cognitive Load Using a Wearable EDA Device

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The inferred cost of work-related stress call for prevention strategies that aim at detecting early warning signs at the workplace. This paper goes one step towards the goal of developing a personal health system for detecting stress. We analyze the discriminative power of electrodermal activity (EDA) in distinguishing stress from cognitive load in an office environment. A collective of 33 subjects underwent a laboratory intervention that included mild cognitive load and two stress factors, which are relevant at the workplace: mental stress induced by solving arithmetic problems under time pressure and psychosocial stress induced by social-evaluative threat. During the experiments, a wearable device was used to monitor the EDA as a measure of the individual stress reaction. Analysis of the data showed that the distributions of the EDA peak height and the instantaneous peak rate carry information about the stress level of a person. Six classifiers were investigated regarding their ability to discriminate cognitive load from stress. A maximum accuracy of 82.8% was achieved for discriminating stress from cognitive load. This would allow keeping track of stressful phases during a working day by using a wearable EDA device.
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paper_title: Towards mental stress detection using wearable physiological sensors

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Early mental stress detection can prevent many stress related health problems. This study aimed at using a wearable sensor system to measure physiological signals and detect mental stress. Three different stress conditions were presented to a healthy subject group. During the procedure, ECG, respiration, skin conductance, and EMG of the trapezius muscles were recorded. In total, 19 physiological features were calculated from these signals. After normalization of the feature values and analysis of correlations among these features, a subset of 9 features was selected for further analysis. Principal component analysis reduced these 9 features to 7 principal components (PCs). Using these PCs and different classifiers, a consistent classification accuracy between stress and non stress conditions of almost 80% was found. This suggests that a promising feature subset was found for future development of a personalized stress monitor.
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paper_title: Detecting stress during real-world driving tasks using physiological sensors

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This paper presents methods for collecting and analyzing physiological data during real-world driving tasks to determine a driver's relative stress level. Electrocardiogram, electromyogram, skin conductance, and respiration were recorded continuously while drivers followed a set route through open roads in the greater Boston area. Data from 24 drives of at least 50-min duration were collected for analysis. The data were analyzed in two ways. Analysis I used features from 5-min intervals of data during the rest, highway, and city driving conditions to distinguish three levels of driver stress with an accuracy of over 97% across multiple drivers and driving days. Analysis II compared continuous features, calculated at 1-s intervals throughout the entire drive, with a metric of observable stressors created by independent coders from videotapes. The results show that for most drivers studied, skin conductivity and heart rate metrics are most closely correlated with driver stress level. These findings indicate that physiological signals can provide a metric of driver stress in future cars capable of physiological monitoring. Such a metric could be used to help manage noncritical in-vehicle information systems and could also provide a continuous measure of how different road and traffic conditions affect drivers.
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paper_title: A Brain-Computer Interface for classifying EEG correlates of chronic mental stress

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In this paper, a Brain-Computer Interface (BCI) for classifying EEG correlates of chronic mental stress is proposed. Data from 8 EEG channels are collected from 26 healthy right-handed students during university examination period and after the examination whereby the former is considered to be relatively more stressful to students than the latter. The mental stress level are measured using the Perceived Stress Scale 14 (PSS-14) and categorized into stressed and stress-free groups. The proposed BCI is then used to classify the subjects' mental stress level on EEG features extracted using the Higuchi's fractal dimension of EEG, Gaussian mixtures of EEG spectrogram, and Magnitude Square Coherence Estimation (MSCE) between the EEG channels. Classification on the EEG features are then performed using the K-Nearest Neighbor (K-NN) and Support Vector Machine (SVM). The performance of the proposed BCI is then evaluated from the inter-subject classification accuracy using leave-one-out validation. The results showed that the proposed BCI using features extracted by MSCE yielded a promising inter-subject validation accuracy of over 90% in classifying the EEG correlates of chronic mental stress.
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paper_title: Tracking Depression Dynamics in College Students Using Mobile Phone and Wearable Sensing

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There are rising rates of depression on college campuses. Mental health services on our campuses are working at full stretch. In response researchers have proposed using mobile sensing for continuous mental health assessment. Existing work on understanding the relationship between mobile sensing and depression, however, focuses on generic behavioral features that do not map to major depressive disorder symptoms defined in the standard mental disorders diagnostic manual (DSM-5). We propose a new approach to predicting depression using passive sensing data from students' smartphones and wearables. We propose a set of symptom features that proxy the DSM-5 defined depression symptoms specifically designed for college students. We present results from a study of 83 undergraduate students at Dartmouth College across two 9-week terms during the winter and spring terms in 2016. We identify a number of important new associations between symptom features and student self reported PHQ-8 and PHQ-4 depression scores. The study captures depression dynamics of the students at the beginning and end of term using a pre-post PHQ-8 and week by week changes using a weekly administered PHQ-4. Importantly, we show that symptom features derived from phone and wearable sensors can predict whether or not a student is depressed on a week by week basis with 81.5% recall and 69.1% precision.
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paper_title: Convolutional Neural Networks for human activity recognition using mobile sensors

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A variety of real-life mobile sensing applications are becoming available, especially in the life-logging, fitness tracking and health monitoring domains. These applications use mobile sensors embedded in smart phones to recognize human activities in order to get a better understanding of human behavior. While progress has been made, human activity recognition remains a challenging task. This is partly due to the broad range of human activities as well as the rich variation in how a given activity can be performed. Using features that clearly separate between activities is crucial. In this paper, we propose an approach to automatically extract discriminative features for activity recognition. Specifically, we develop a method based on Convolutional Neural Networks (CNN), which can capture local dependency and scale invariance of a signal as it has been shown in speech recognition and image recognition domains. In addition, a modified weight sharing technique, called partial weight sharing, is proposed and applied to accelerometer signals to get further improvements. The experimental results on three public datasets, Skoda (assembly line activities), Opportunity (activities in kitchen), Actitracker (jogging, walking, etc.), indicate that our novel CNN-based approach is practical and achieves higher accuracy than existing state-of-the-art methods.
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paper_title: Towards unravelling the relationship between on-body, environmental and emotion data using sensor information fusion approach

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Abstract Over the past few years, there has been a noticeable advancement in environmental models and information fusion systems taking advantage of the recent developments in sensor and mobile technologies. However, little attention has been paid so far to quantifying the relationship between environment changes and their impact on our bodies in real-life settings. In this paper, we identify a data driven approach based on direct and continuous sensor data to assess the impact of the surrounding environment and physiological changes and emotion. We aim at investigating the potential of fusing on-body physiological signals, environmental sensory data and on-line self-report emotion measures in order to achieve the following objectives: (1) model the short term impact of the ambient environment on human body, (2) predict emotions based on-body sensors and environmental data. To achieve this, we have conducted a real-world study ‘in the wild’ with on-body and mobile sensors. Data was collected from participants walking around Nottingham city centre, in order to develop analytical and predictive models. Multiple regression, after allowing for possible confounders, showed a noticeable correlation between noise exposure and heart rate. Similarly, UV and environmental noise have been shown to have a noticeable effect on changes in ElectroDermal Activity (EDA ). Air pressure demonstrated the greatest contribution towards the detected changes in body temperature and motion. Also, significant correlation was found between air pressure and heart rate . Finally, decision fusion of the classification results from different modalities is performed. To the best of our knowledge this work presents the first attempt at fusing and modelling data from environmental and physiological sources collected from sensors in a real-world setting.
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paper_title: Relation between mental stress-induced prefrontal cortex activity and skin conditions: A near-infrared spectroscopy study

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Abstract Although psychological stress affects skin condition, the neurophysiological mechanism involved is unclear. In this study, we evaluated the relationship between skin condition and left/right asymmetry in prefrontal cortex (PFC) activity during mental stress tasks since recent studies have suggested that the right PFC dominates the regulation of the stress response system, including the hypothalamic–pituitary–adrenal (HPA) axis. Using near-infrared spectroscopy, we measured hemoglobin concentration changes in the bilateral PFC during a mental arithmetic task in normal adults and evaluated the laterality scores (i.e., [(right − left) / (right + left)]) of oxyhemoglobin concentration changes. Elicitation of stress was verified by the State-Trait Anxiety Inventory (STAI) and heart rate. The sebum levels and Propionibacterium acnes populations in the facial skin were measured before the task. The task significantly increased the STAI-II scores (p = 0.00079) and heart rate (p = 0.0000049). The oxyhemoglobin concentration increased in the bilateral PFC during the task, associated with a decrease in deoxyhemoglobin concentration. The laterality scores of oxyhemoglobin concentration changes were positively correlated with sebum levels (r = + 0.50, p = 0.026) and P. acnes populations (r = + 0.49, p = 0.029) in the facial skin before the task. There was a significant positive correlation between heart rate changes and the laterality scores of oxyhemoglobin concentration changes (r = + 0.54, p = 0.015). These results demonstrate that the subjects with higher sebum levels and higher P. acnes populations in the facial skin have a right dominant PFC activity during a mental stress task and suggest that such subjects are sensitive to mental stress associated with hyperactivity of the stress response system, including the HPA axis system.
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paper_title: Objective measures, sensors and computational techniques for stress recognition and classification

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Stress is a major growing concern in our day and age adversely impacting both individuals and society. Stress research has a wide range of benefits from improving personal operations, learning, and increasing work productivity to benefiting society - making it an interesting and socially beneficial area of research. This survey reviews sensors that have been used to measure stress and investigates techniques for modelling stress. It discusses non-invasive and unobtrusive sensors for measuring computed stress, a term we coin in the paper. Sensors that do not impede everyday activities that could be used by those who would like to monitor stress levels on a regular basis (e.g. vehicle drivers, patients with illnesses linked to stress) is the focus of the discussion. Computational techniques have the capacity to determine optimal sensor fusion and automate data analysis for stress recognition and classification. Several computational techniques have been developed to model stress based on techniques such as Bayesian networks, artificial neural networks, and support vector machines, which this survey investigates. The survey concludes with a summary and provides possible directions for further computational stress research.
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paper_title: Deep Recurrent Neural Networks for Human Activity Recognition

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Adopting deep learning methods for human activity recognition has been effective in extracting discriminative features from raw input sequences acquired from body-worn sensors. Although human movements are encoded in a sequence of successive samples in time, typical machine learning methods perform recognition tasks without exploiting the temporal correlations between input data samples. Convolutional neural networks (CNNs) address this issue by using convolutions across a one-dimensional temporal sequence to capture dependencies among input data. However, the size of convolutional kernels restricts the captured range of dependencies between data samples. As a result, typical models are unadaptable to a wide range of activity-recognition configurations and require fixed-length input windows. In this paper, we propose the use of deep recurrent neural networks (DRNNs) for building recognition models that are capable of capturing long-range dependencies in variable-length input sequences. We present unidirectional, bidirectional, and cascaded architectures based on long short-term memory (LSTM) DRNNs and evaluate their effectiveness on miscellaneous benchmark datasets. Experimental results show that our proposed models outperform methods employing conventional machine learning, such as support vector machine (SVM) and k-nearest neighbors (KNN). Additionally, the proposed models yield better performance than other deep learning techniques, such as deep believe networks (DBNs) and CNNs.
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paper_title: Deep learning analysis of mobile physiological, environmental and location sensor data for emotion detection

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The detection and monitoring of emotions are important in various applications, e.g. to enable naturalistic and personalised human-robot interaction. Emotion detection often require modelling of various data inputs from multiple modalities, including physiological signals (e.g.EEG and GSR), environmental data (e.g. audio and weather), videos (e.g. for capturing facial expressions and gestures) and more recently motion and location data. Many traditional machine learning algorithms have been utilised to capture the diversity of multimodal data at the sensors and features levels for human emotion classification. While the feature engineering processes often embedded in these algorithms are beneficial for emotion modelling, they inherit some critical limitations which may hinder the development of reliable and accurate models. In this work, we adopt a deep learning approach for emotion classification through an iterative process by adding and removing large number of sensor signals from different modalities. Our dataset was collected in a real-world study from smart-phones and wearable devices. It merges local interaction of three sensor modalities: on-body, environmental and location into global model that represents signal dynamics along with the temporal relationships of each modality. Our approach employs a series of learning algorithms including a hybrid approach using Convolutional Neural Network and Long Short-term Memory Recurrent Neural Network (CNN-LSTM) on the raw sensor data, eliminating the needs for manual feature extraction and engineering. The results show that the adoption of deep-learning approaches is effective in human emotion classification when large number of sensors input is utilised (average accuracy 95% and F-Measure=%95) and the hybrid models outperform traditional fully connected deep neural network (average accuracy 73% and F-Measure=73%). Furthermore, the hybrid models outperform previously developed Ensemble algorithms that utilise feature engineering to train the model average accuracy 83% and F-Measure=82%)
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paper_title: The Advantages of Wearable Green Reflected Photoplethysmography

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This report evaluates the efficacy of reflected-type green light photoplethysmography (green light PPG). Transmitted infrared light was used for PPG and the arterial pulse was monitored transcutaneously. The reflected PPG signal contains AC components based on the heartbeat-related signal from the arterial blood flow and DC components, which include reflectance and scattering from tissue. Generally, changes in AC components are monitored, but the DC components play an important role during heat stress. In this study, we compared the signal of green light PPG to infrared PPG and ECG during heat stress. The wavelengths of the green and infrared light were 525 nm and 880 nm, respectively. Experiments were performed on young healthy subjects in cold (10°C), hot (45°C), and normal environments. The pulse rates were compared among three measurement devices and the AC and DC components of the PPG signal were evaluated during heat stress. The pulse rates obtained from green light PPG were strongly correlated with the R---R interval of an electrocardiogram in all environments, but those obtained from infrared light PPG displayed a weaker correlation with cold exposure. The AC components were of similar signal output for both wavelengths during heat stress. Also, the DC components for green light PPG were similar during heat stress, but showed less signal output for infrared light PPG during hot exposure. The main reason for the reduced DC components was speculated to be the increased blood flow at the vascular bed. Therefore, reflected green light PPG can be useful for pulse rate monitoring because it is less influenced by the tissue and vein region.
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paper_title: ECG-based Mental Stress Assessment Using Fuzzy Computing and Associative Petri Net

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Medical reports suggest that long-term stress can directly or indirectly lead to mental disorders and cardiovascular disease. However, many people frequently ignore their stress symptoms and fail to take remedial action before developing serious psychological or physiological health problems. Many previous studies have used electrocardiograms (ECG) to evaluate mental stress. However, ECG pattern recognition presents difficulties because the time-varying morphology is subject to physiological conditions and the presence of noise. Therefore, how to achieve effective noise reduction and accurately determine an individual’s exposure to mental stress under various conditions is a recurring issue in engineering and medical research. Heart rate variability is assessed using time- and frequency-domain analyses. To accurately detect mental stress, this research adopts both time and frequency domains, two aspects of physiological characteristics. In this study, a rule-based reasoning model is created for mental stress assessment by combining fuzzy and associative Petri net methodologies. It can serve as a basis for clinical diagnosis and quickly measure the subject’s mental condition and reduce subjective errors. Performance evaluation using Physionet’s stress recognition database shows that the proposed approach compares well with data mining methods and other proposed methods. Moreover, a prototype mental stress assessment system is proposed to help users understand their mental condition.
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paper_title: Stress Recognition Using Wearable Sensors and Mobile Phones

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In this study, we aim to find physiological or behavioral markers for stress. We collected 5 days of data for 18 participants: a wrist sensor (accelerometer and skin conductance), mobile phone usage (call, short message service, location and screen on/off) and surveys (stress, mood, sleep, tiredness, general health, alcohol or caffeinated beverage intake and electronics usage). We applied correlation analysis to find statistically significant features associated with stress and used machine learning to classify whether the participants were stressed or not. In comparison to a baseline 87.5% accuracy using the surveys, our results showed over 75% accuracy in a binary classification using screen on, mobility, call or activity level information (some showed higher accuracy than the baseline). The correlation analysis showed that the higher-reported stress level was related to activity level, SMS and screen on/off patterns.
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paper_title: Detecting Bipolar Depression From Geographic Location Data

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Objective: This paper aims to identify periods of depression using geolocation movements recorded from mobile phones in a prospective community study of individuals with bipolar disorder (BD). M ethods: Anonymized geographic location recordings from 22 BD participants and 14 healthy controls (HC) were collected over 3 months. Participants reported their depressive symptomatology using a weekly questionnaire (QIDS-SR16). Recorded location data were preprocessed by detecting and removing imprecise data points and features were extracted to assess the level and regularity of geographic movements of the participant. A subset of features were selected using a wrapper feature selection method and presented to 1) a linear regression model and a quadratic generalized linear model with a logistic link function for questionnaire score estimation; and 2) a quadratic discriminant analysis classifier for depression detection in BD participants based on their questionnaire responses. R esults: HC participants did not report depressive symptoms and their features showed similar distributions to nondepressed BD participants. Questionnaire score estimation using geolocation-derived features from BD participants demonstrated an optimal mean absolute error rate of 3.73, while depression detection demonstrated an optimal (median $\pm$ IQR) $\text{F}_{1}$ score of 0.857 $\pm$ 0.022 using five features (classification accuracy: 0.849 $\pm$ 0.016; sensitivity: 0.839 $\pm$ 0.014; specificity: 0.872 $\pm$ 0.047). C onclusion: These results demonstrate a strong link between geographic movements and depression in bipolar disorder. S ignificance: To our knowledge, this is the first community study of passively recorded objective markers of depression in bipolar disorder of this scale. The techniques could help individuals monitor their depression and enable healthcare providers to detect those in need of care or treatment.
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paper_title: MoodExplorer: Towards Compound Emotion Detection via Smartphone Sensing

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Social psychology and neuroscience had confirmed that emotion state exerts a significant effect on human communication, perception, social behavior and decision making. With the wide availability of smartphones equipped with microphone, accelerometer, GPS, and other source of sensors, it is worthwhile to explore the possibility of automatic emotion detection via smartphone sensing. Particularly, we focus on a novel research problem that tries to detect the compound emotion (a set of multiple dimensional basic emotions) of smartphone users. We observe that users' self-reported emotional states have high correlation with their smartphone usage patterns and sensing data. Based on the observations, we exploit a feature extraction and selection algorithm to find the most significant features. We further adopt a factor graph model to tackle the correlations between features and emotion labels, and propose a machine learning algorithm for compound emotion detection based on the smartphone sensing data. The proposed mechanism is implemented as an APP called MoodExplorer in Android platform. Extensive experiments conducted on the smartphone data collected from 30 university students show that MoodExplorer can recognize users' compound emotions with 76.0% exact match on average.
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paper_title: A Survey of Deep Learning: Platforms, Applications and Emerging Research Trends

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Deep learning has exploded in the public consciousness, primarily as predictive and analytical products suffuse our world, in the form of numerous human-centered smart-world systems, including targeted advertisements, natural language assistants and interpreters, and prototype self-driving vehicle systems. Yet to most, the underlying mechanisms that enable such human-centered smart products remain obscure. In contrast, researchers across disciplines have been incorporating deep learning into their research to solve problems that could not have been approached before. In this paper, we seek to provide a thorough investigation of deep learning in its applications and mechanisms. Specifically, as a categorical collection of state of the art in deep learning research, we hope to provide a broad reference for those seeking a primer on deep learning and its various implementations, platforms, algorithms, and uses in a variety of smart-world systems. Furthermore, we hope to outline recent key advancements in the technology, and provide insight into areas, in which deep learning can improve investigation, as well as highlight new areas of research that have yet to see the application of deep learning, but could nonetheless benefit immensely. We hope this survey provides a valuable reference for new deep learning practitioners, as well as those seeking to innovate in the application of deep learning.
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paper_title: An ultra-brief screening scale for anxiety and depression: the PHQ-4.

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BACKGROUND ::: The most common mental disorders in both outpatient settings and the general population are depression and anxiety, which frequently coexist. Both of these disorders are associated with considerable disability. ::: ::: ::: OBJECTIVE ::: When the disorders co-occur, the disability is even greater. Authors sought to test an ultra-brief screening tool for both. ::: ::: ::: METHOD ::: Validated two-item ultra-brief screeners for depression and anxiety were combined to constitute the Patient Health Questionnaire for Depression and Anxiety (the PHQ-4). Data were analyzed from 2,149 patients drawn from 15 primary-care clinics in the United States. ::: ::: ::: RESULTS ::: Factor analysis confirmed two discrete factors (Depression and Anxiety) that explained 84% of the total variance. Increasing PHQ-4 scores were strongly associated with functional impairment, disability days, and healthcare use. Anxiety had a substantial effect on functional status that was independent of depression. ::: ::: ::: CONCLUSION ::: The PHQ-4 is a valid ultra-brief tool for detecting both anxiety and depressive disorders.
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paper_title: Multimodal Deep Learning for Activity and Context Recognition

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Wearables and mobile devices see the world through the lens of half a dozen low-power sensors, such as, barometers, accelerometers, microphones and proximity detectors. But differences between sensors ranging from sampling rates, discrete and continuous data or even the data type itself make principled approaches to integrating these streams challenging. How, for example, is barometric pressure best combined with an audio sample to infer if a user is in a car, plane or bike? Critically for applications, how successfully sensor devices are able to maximize the information contained across these multi-modal sensor streams often dictates the fidelity at which they can track user behaviors and context changes. This paper studies the benefits of adopting deep learning algorithms for interpreting user activity and context as captured by multi-sensor systems. Specifically, we focus on four variations of deep neural networks that are based either on fully-connected Deep Neural Networks (DNNs) or Convolutional Neural Networks (CNNs). Two of these architectures follow conventional deep models by performing feature representation learning from a concatenation of sensor types. This classic approach is contrasted with a promising deep model variant characterized by modality-specific partitions of the architecture to maximize intra-modality learning. Our exploration represents the first time these architectures have been evaluated for multimodal deep learning under wearable data -- and for convolutional layers within this architecture, it represents a novel architecture entirely. Experiments show these generic multimodal neural network models compete well with a rich variety of conventional hand-designed shallow methods (including feature extraction and classifier construction) and task-specific modeling pipelines, across a wide-range of sensor types and inference tasks (four different datasets). Although the training and inference overhead of these multimodal deep approaches is in some cases appreciable, we also demonstrate the feasibility of on-device mobile and wearable execution is not a barrier to adoption. This study is carefully constructed to focus on multimodal aspects of wearable data modeling for deep learning by providing a wide range of empirical observations, which we expect to have considerable value in the community. We summarize our observations into a series of practitioner rules-of-thumb and lessons learned that can guide the usage of multimodal deep learning for activity and context detection.
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paper_title: Virtual reality exposure therapy for anxiety disorders: A meta-analysis

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Abstract There is now a substantial literature investigating virtual reality exposure therapy (VRET) as a viable treatment option for anxiety disorders. In this meta-analysis we provide effect size estimates for virtual reality treatment in comparison to in vivo exposure and control conditions (waitlist, attention control, etc.). A comprehensive search of the literature identified 13 studies ( n = 397) that were included in the final analyses. Consistent with prediction the primary random effects analysis showed a large mean effect size for VRET compared to control conditions, Cohen's d = 1.11 (S.E. = 0.15, 95% CI: 0.82–1.39). This finding was consistent across secondary outcome categories as well (domain-specific, general subjective distress, cognition, behavior, and psychophysiology). Also as expected in vivo treatment was not significantly more effective than VRET. In fact, there was a small effect size favoring VRET over in vivo conditions, Cohen's d = 0.35 (S.E. = 0.15, 95% CI: 0.05–0.65). There was a trend for a dose–response relationship with more VRET sessions showing larger effects ( p = 0.06). Outcome was not related to publication year or sample size. Implications are discussed.
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paper_title: Can virtual reality exposure therapy gains be generalized to real-life? : A meta-analysis of studies applying behavioral assessments

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In virtual reality exposure therapy (VRET), patients are exposed to virtual environments that resemble feared real-life situations. The aim of the current study was to assess the extent to which VRET gains can be observed in real-life situations. We conducted a meta-analysis of clinical trials applying VRET to specific phobias and measuring treatment outcome by means of behavioral laboratory tests or recordings of behavioral activities in real-life. Data sources were searches of databases (Medline, PsycInfo, and Cochrane). We included in total 14 clinical trials on specific phobias. Results revealed that patients undergoing VRET did significantly better on behavioral assessments following treatment than before treatment, with an aggregated uncontrolled effect size of g = 1.23. Furthermore, patients undergoing VRET performed better on behavioral assessments at post-treatment than patients on wait-list (g = 1.41). Additionally, results of behavioral assessment at post-treatment and at follow-up revealed no significant differences between VRET and exposure in vivo (g = -0.09 and 0.53, respectively). Finally, behavioral measurement effect sizes were similar to those calculated from self-report measures. The findings demonstrate that VRET can produce significant behavior change in real-life situations and support its application in treating specific phobias.
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paper_title: Virtual Reality Exposure Therapy for Post-Traumatic Stress Disorder and Other Anxiety Disorders

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Anxiety disorders, including phobias and post-traumatic stress disorder, are common and disabling disorders that often involve avoidance behavior. Cognitive-behavioral treatments, specifically imaginal and in vivo forms of exposure therapy, have been accepted and successful forms of treatment for these disorders. Virtual reality exposure therapy, an alternative to more traditional exposure-based therapies, involves immersion in a computer-generated virtual environment that minimizes avoidance and facilitates emotional processing. In this article, we review evidence on the application of virtual reality exposure therapy to the treatment of specific phobias and post-traumatic stress disorder and discuss its advantages and cautions.
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paper_title: Virtual reality exposure therapy in anxiety disorders: a systematic review of process-and-outcome studies

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In recent years, virtual reality exposure therapy (VRET) has become an interesting alternative for the treatment of anxiety disorders. Research has focused on the efficacy of VRET in treating anxiety disorders: phobias, panic disorder, and posttraumatic stress disorder. In this systematic review, strict methodological criteria are used to give an overview of the controlled trials regarding the efficacy of VRET in patients with anxiety disorders. Furthermore, research into process variables such as the therapeutic alliance and cognitions and enhancement of therapy effects through cognitive enhancers is discussed. The implications for implementation into clinical practice are considered.
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paper_title: A Wearable Social Interaction Aid for Children with Autism

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Over 1 million children under the age of 17 in the US have been identified with Autism Spectrum Disorder (ASD). These children struggle to recognize facial expressions, make eye contact, and engage in social interactions. Gaining these skills requires intensive behavioral interventions that are often expensive, difficult to access, and inconsistently administered.nWe have developed a system to automate facial expression recognition that runs on wearable glasses and delivers real time social cues, with the goal of creating a behavioral aid for children with ASD that maximizes behavioral feedback while minimizing the distractions to the child. This paper describes the design of our system and interface decisions resulting from initial observations gathered during multiple preliminary trials.
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paper_title: Virtual reality in mental health

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Background ::: Several virtual reality (VR) applications for the understanding, assessment and treatment of mental health problems have been developed in the last 10 years. The purpose of this review is to outline the current state of virtual reality research in the treatment of mental health problems.
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paper_title: Heart rate variability biofeedback as a behavioral neurocardiac intervention to enhance vagal heart rate control

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Background Patients with coronary heart disease (CHD) who experience depressed mood or psychological stress exhibit decreased vagal control of heart rate (HR), as assessed by spectral analysis of HR variability (HRV). Myocardial infarction and sudden cardiac death are independently associated with depression and stress, as well as impaired vagal HR control. This study examined whether a behavioral neurocardiac intervention to reduce stress or depression can augment cardiovagal modulation in CHD patients. We hypothesized that (1) cognitive-behavioral training with HRV biofeedback would augment vagal recovery from acute stress, and (2) vagal regulation of HR would be inversely associated with stress and depression after treatment. Methods This randomized controlled trial enrolled 46 CHD patients from 3 clinics of CHD risk reduction in Toronto and Vancouver, Canada. Subjects were randomized to five 1.5-hour sessions of HRV biofeedback or an active control condition. Outcome was assessed by absolute and normalized high-frequency spectral components (0.15-0.50 Hz) of HRV, and by the Perceived Stress Scale and Centre for Epidemiologic Studies in Depression scale. Results Both groups reduced symptoms on the Perceived Stress Scale ( P = .001) and Centre for Epidemiologic Studies in Depression scale ( P = .004). Hierarchical linear regression determined that improved psychological adjustment was significantly associated with the high-frequency index of vagal HR modulation only in the HRV biofeedback group. Adjusted R 2 was as follows: HRV biofeedback group, 0.86 for stress ( P = .02) and 0.81 for depression ( P = .03); versus the active control group, 0.04 ( P = .57) and 0.13 ( P = .95), respectively. Conclusion A novel behavioral neurocardiac intervention, HRV biofeedback, can augment vagal HR regulation while facilitating psychological adjustment to CHD.
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paper_title: Biofeedback of Heart Rate Variability and Related Physiology: A Critical Review

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Low heart rate variability (HRV) characterizes several medical and psychological diseases. HRV biofeedback is a newly developed approach that may have some use for treating the array of disorders in which HRV is relatively low. This review critically appraises evidence for the effectiveness of HRV and related biofeedback across 14 studies in improving (1) HRV and baroreflex outcomes and (2) clinical outcomes. Results revealed that HRV biofeedback consistently effectuates acute improvements during biofeedback practice, whereas the presence of short-term and long-term carry-over effects is less clear. Some evidence suggests HRV biofeedback may result in long-term carry-over effects on baroreflex gain, which is an area most promising for future investigations. On the other hand, concerning clinical outcomes, there is ample evidence attesting to efficacy of HRV biofeedback. However, because clinical and physiological outcomes do not improve concurrently in all cases, the mechanism by which HRV biofeedback results in salutary effects in unclear. Considerations for the field in addressing shortcomings of the reviewed studies and advancing understanding of the way in which HRV biofeedback may improve physiological and clinical outcomes are offered in light of the reviewed evidence.
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paper_title: Remote Biofeedback Sharing, Opportunities and Challenges

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Biofeedback is commonly used to regulate one's state, for example to manage stress. The underlying idea is that by perceiving a feedback about their physiological activity, a user can act upon it. In this paper we describe through two recent projects how biofeedback could be leveraged to share one's state at distance. Such extension of biofeedback could answer to the need of belonging, further widening the applications of the technology in terms of well-being.
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paper_title: Massage Therapy as a Workplace Intervention for Reduction of Stress

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This study evaluated the effectiveness of a 15-min. on-site massage while seated in a chair on reducing stress as indicated by blood pressure. 52 employed participants' blood pressures were measured before and after a 15-min. massage at work. Analysis showed a significant reduction in participants' systolic and diastolic blood pressure after receiving the massage although there was no control group.
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paper_title: Design of haptic interfaces for therapy

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Touch is fundamental to our emotional well-being. Medical science is starting to understand and develop touch-based therapies for autism spectrum, mood, anxiety and borderline disorders. Based on the most promising touch therapy protocols, we are presenting the first devices that simulate touch through haptic devices to bring relief and assist clinical therapy for mental health. We present several haptic systems that enable medical professionals to facilitate the collaboration between patients and doctors and potentially pave the way for a new form of non-invasive treatment that could be adapted from use in care-giving facilities to public use. We developed these prototypes working closely with a team of mental health professionals.
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paper_title: EmoEcho: A Tangible Interface to Convey and Communicate Emotions

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An interactive tangible interface has been developed to capture and communicate emotions between people who are missing and longing for loved ones. EmoEcho measures the wearer's pulse, touch and movement to provide varying vibration patterns on the partner device. During an informal evaluation of two prototype devices users acknowledged how EmoEcho could help counter the negative feeling of missing someone through the range of haptic feedback offered. In general, we believe, tangible interfaces appear to offer a non-obtrusive means towards interpreting and communicating emotions to others.
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paper_title: The calming effect of a new wearable device during the anticipation of public speech

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We assessed the calming effect of doppel, a wearable device that delivers heartbeat-like tactile stimulation on the wrist. We tested whether the use of doppel would have a calming effect on physiological arousal and subjective reports of state anxiety during the anticipation of public speech, a validated experimental task that is known to induce anxiety. Two groups of participants were tested in a single-blind design. Both groups wore the device on their wrist during the anticipation of public speech, and were given the cover story that the device was measuring blood pressure. For only one group, the device was turned on and delivered a slow heartbeat-like vibration. Participants in the doppel active condition displayed lower increases in skin conductance responses relative to baseline and reported lower anxiety levels compared to the control group. Therefore, the presence, as opposed to its absence, of a slow rhythm, which in the present study was instantiated as an auxiliary slow heartbeat delivered through doppel, had a significant calming effect on physiological arousal and subjective experience during a socially stressful situation. This finding is discussed in relation to past research on responses and entrainment to rhythms, and their effects on arousal and mood.
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paper_title: Stress and EEG

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Many people suffer from stress in their everyday life. While there is a close relationship between stress and mental health, psychological stress (and associated emotions such as anger, anxiety, and depression), can also have effects on physical health. Indeed, chronic psychological stress can change the responsiveness of central-peripheral regulatory systems (Fuchs & Fluegee 1995; Fuchs, Uno, & Fluegge 1995), potentially rendering them less efficient or adaptive in terms of supporting health. Conditions such as chronic stress, depression, and anxiety have been found to be associated with abnormal autonomic nervous system (ANS) functioning (Cohen et al., 2000; Hughes & Stoney 2000). Accordingly, stress is one of the major factors contributing to chronic disorders (Decker et al., 1996; Lawrence & Kim 2000). Stress also influences the desire to work, performance at work, and one’s general attitude toward life (NIOSH, 1999). Within the industry sector, higher stress levels and stress-related disease lead to decrease in company performance and increase in medical expenses (Cooper, 1996; Manning, et al., 1996). In 2008 in Korea, there were 1,967 deaths associated with occupational cases. Almost half of these deaths (974, or 49.5%) could be ascribed to heart or brain blood vessel disease (Ministry of Labor, 2008). This is not surprising given that stress is implicated in 75% of all heart and brain blood vessel diseases (Belkic, et al., 2004). It is thus apparent that stress can increase social and economic losses and decrease a country’s competitiveness (Driskell & Salas, 1996). Therefore, precautionary measures to reduce stress and adequate management of this condition are essential for both individual health and the welfare of society at a broader level. This chapter reports on relationships among psychological stress, the EEG (Electroencephalogram), ECG, and salivary cortisol in people suffering from chronic stress. We hypothesized that chronic stress will have negative effects on the central-ANS and physiological responsiveness. There are many bio-signal channels by which stress can be potentially quantified, including ECG, EEG, and the skin conductance response (SCR) (Kohlish & Schaefer 1996; Gevins et al., 1998). Even so, determining the stress level of any given individual can be difficult. Related to this is debate concerning the extent to which the EEG can be used to reliably measure stress. However, this chapter presents data showing that there are significant correlations between EEG measures and other indices of stress, including the ECG and salivary cortisol. Also, it revealed relationships between high beta Source: Convergence and Hybrid Information Technologies, Book edited by: Marius Crisan, ISBN 978-953-307-068-1, pp. 426, March 2010, INTECH, Croatia, downloaded from SCIYO.COM
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paper_title: Mood regulation in depression: Differential effects of distraction and recall of happy memories on sad mood.

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Recent research suggests that the recall of positive memories plays an important role in mood regulation. In this study, the authors examined the ability of currently depressed, formerly depressed, and neverdepressed participants to regulate sad mood through the recall of positive memories or through distraction. Although improvement in mood was found for all participants in response to distraction, under instructions to recall positive memories, never-depressed participants’ moods improved, whereas formerly depressed participants’ sad moods remained unchanged. It is important to note that depressed participants exhibited a worsening of their sad moods after recalling positive memories. These results suggest both that depression is associated with an impaired ability to use positive recall to regulate a sad mood and that this impairment continues to be evident following recovery.
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paper_title: Squeeze, rock, and roll; can tangible interaction with affective products support stress reduction?

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Affective computing focuses on the interpretation of users emotions via physiological and behavioral inputs. Irrelevant gestures with a pen were found to increase when users were given a mentally demanding task. Accordingly, an embedded tangible interface was developed which afforded and measured a rolling behavior, and guided the user towards reaching a balanced state of movement. During informal evaluations users acknowledged how the device could contribute to stress reduction. Conclusion, tangible interfaces appear to offer a non-obtrusive means towards interpreting and reducing stress in the office work context.
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paper_title: Patients' techniques for coping with schizophrenia: An exploratory study

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Non-hospitalized schizophrenic patients were administered a questionnaire designed to assess how they coped with various symptoms of their illness. Of the 200 subjects in the sample, 92 gave additional descriptions of coping techniques which they found useful in addition to those listed in the questionnaire. This report presents the latter findings and categorizes the range of coping techniques described. Patients used a variety of such techniques ranging across a spectrum of behaviour change, socialization, cognitive control and use of medical care as well as behaviours which were likely to have been identified by others as symptomatic of the illness.
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paper_title: Vector Space Representation of Bluetooth Encounters for Mental Health Inference

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Social interactions have multifaceted effects on individuals' mental health statuses, including mood and stress. As a proxy for the social environment, Bluetooth encounters detected by personal mobile devices have been used to improve mental health prediction and have shown preliminary success. In this paper, we propose a vector space model representation of Bluetooth encounters in which we convert encounters into spatiotemporal tokens within a multidimensional feature space. We discuss multiple token designs and feature value schemes and evaluate the predictive power of the resulting features for stress recognition tasks using the StudentLife and Friends & Family datasets. Our findings motivate further discussion and research on bag-of-words approaches for representing raw mobile sensing signals for health outcome inference.
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paper_title: Robot Therapy: A New Approach for Mental Healthcare of the Elderly – A Mini-Review

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Mental healthcare of elderly people is a common problem in advanced countries. Recently, high technology has developed robots for use not only in factories but also for our living environment. In particular, human-interactive robots for psychological enrichment, which provide services by interacting with humans while stimulating their minds, are rapidly spreading. Such robots not only simply entertain but also render assistance, guide, provide therapy, educate, enable communication, and so on. Robot therapy, which uses robots as a substitution for animals in animal-assisted therapy and activity, is a new application of robots and is attracting the attention of many researchers and psychologists. The seal robot named Paro was developed especially for robot therapy and was used at hospitals and facilities for elderly people in several countries. Recent research has revealed that robot therapy has the same effects on people as animal therapy. In addition, it is being recognized as a new method of mental healthcare for elderly people. In this mini review, we introduce the merits and demerits of animal therapy. Then we explain the human-interactive robot for psychological enrichment, the required functions for therapeutic robots, and the seal robot. Finally, we provide examples of robot therapy for elderly people, including dementia patients.
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paper_title: Next-generation psychiatric assessment: Using smartphone sensors to monitor behavior and mental health.

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OBJECTIVE ::: Optimal mental health care is dependent upon sensitive and early detection of mental health problems. We have introduced a state-of-the-art method for the current study for remote behavioral monitoring that transports assessment out of the clinic and into the environments in which individuals negotiate their daily lives. The objective of this study was to examine whether the information captured with multimodal smartphone sensors can serve as behavioral markers for one's mental health. We hypothesized that (a) unobtrusively collected smartphone sensor data would be associated with individuals' daily levels of stress, and (b) sensor data would be associated with changes in depression, stress, and subjective loneliness over time. ::: ::: ::: METHOD ::: A total of 47 young adults (age range: 19-30 years) were recruited for the study. Individuals were enrolled as a single cohort and participated in the study over a 10-week period. Participants were provided with smartphones embedded with a range of sensors and software that enabled continuous tracking of their geospatial activity (using the Global Positioning System and wireless fidelity), kinesthetic activity (using multiaxial accelerometers), sleep duration (modeled using device-usage data, accelerometer inferences, ambient sound features, and ambient light levels), and time spent proximal to human speech (i.e., speech duration using microphone and speech detection algorithms). Participants completed daily ratings of stress, as well as pre- and postmeasures of depression (Patient Health Questionnaire-9; Spitzer, Kroenke, & Williams, 1999), stress (Perceived Stress Scale; Cohen et al., 1983), and loneliness (Revised UCLA Loneliness Scale; Russell, Peplau, & Cutrona, 1980). ::: ::: ::: RESULTS ::: Mixed-effects linear modeling showed that sensor-derived geospatial activity (p < .05), sleep duration (p < .05), and variability in geospatial activity (p < .05), were associated with daily stress levels. Penalized functional regression showed associations between changes in depression and sensor-derived speech duration (p < .05), geospatial activity (p < .05), and sleep duration (p < .05). Changes in loneliness were associated with sensor-derived kinesthetic activity (p < .01). ::: ::: ::: CONCLUSIONS AND IMPLICATIONS FOR PRACTICE ::: Smartphones can be harnessed as instruments for unobtrusive monitoring of several behavioral indicators of mental health. Creative leveraging of smartphone sensing could provide novel opportunities for close-to-invisible psychiatric assessment at a scale and efficiency that far exceeds what is currently feasible with existing assessment technologies.
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paper_title: Remote Biofeedback Sharing, Opportunities and Challenges

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Biofeedback is commonly used to regulate one's state, for example to manage stress. The underlying idea is that by perceiving a feedback about their physiological activity, a user can act upon it. In this paper we describe through two recent projects how biofeedback could be leveraged to share one's state at distance. Such extension of biofeedback could answer to the need of belonging, further widening the applications of the technology in terms of well-being.
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paper_title: A Survey on Wearable Sensor-Based Systems for Health Monitoring and Prognosis

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The design and development of wearable biosensor systems for health monitoring has garnered lots of attention in the scientific community and the industry during the last years. Mainly motivated by increasing healthcare costs and propelled by recent technological advances in miniature biosensing devices, smart textiles, microelectronics, and wireless communications, the continuous advance of wearable sensor-based systems will potentially transform the future of healthcare by enabling proactive personal health management and ubiquitous monitoring of a patient's health condition. These systems can comprise various types of small physiological sensors, transmission modules and processing capabilities, and can thus facilitate low-cost wearable unobtrusive solutions for continuous all-day and any-place health, mental and activity status monitoring. This paper attempts to comprehensively review the current research and development on wearable biosensor systems for health monitoring. A variety of system implementations are compared in an approach to identify the technological shortcomings of the current state-of-the-art in wearable biosensor solutions. An emphasis is given to multiparameter physiological sensing system designs, providing reliable vital signs measurements and incorporating real-time decision support for early detection of symptoms or context awareness. In order to evaluate the maturity level of the top current achievements in wearable health-monitoring systems, a set of significant features, that best describe the functionality and the characteristics of the systems, has been selected to derive a thorough study. The aim of this survey is not to criticize, but to serve as a reference for researchers and developers in this scientific area and to provide direction for future research improvements.
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paper_title: Ecological Momentary Assessment

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Assessment in clinical psychology typically relies on global retrospective self-reports collected at research or clinic visits, which are limited by recall bias and are not well suited to address how behavior changes over time and across contexts. Ecological momentary assessment (EMA) involves repeated sampling of subjects’ current behaviors and experiences in real time, in subjects’ natural environments. EMA aims to minimize recall bias, maximize ecological validity, and allow study of microprocesses that influence behavior in real-world contexts. EMA studies assess particular events in subjects’ lives or assess subjects at periodic intervals, often by random time sampling, using technologies ranging from written diaries and telephones to electronic diaries and physiological sensors. We discuss the rationale for EMA, EMA designs, methodological and practical issues, and comparisons of EMA and recall data. EMA holds unique promise to advance the science and practice of clinical psychology by shedding ligh...
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paper_title: Detecting stress during real-world driving tasks using physiological sensors

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This paper presents methods for collecting and analyzing physiological data during real-world driving tasks to determine a driver's relative stress level. Electrocardiogram, electromyogram, skin conductance, and respiration were recorded continuously while drivers followed a set route through open roads in the greater Boston area. Data from 24 drives of at least 50-min duration were collected for analysis. The data were analyzed in two ways. Analysis I used features from 5-min intervals of data during the rest, highway, and city driving conditions to distinguish three levels of driver stress with an accuracy of over 97% across multiple drivers and driving days. Analysis II compared continuous features, calculated at 1-s intervals throughout the entire drive, with a metric of observable stressors created by independent coders from videotapes. The results show that for most drivers studied, skin conductivity and heart rate metrics are most closely correlated with driver stress level. These findings indicate that physiological signals can provide a metric of driver stress in future cars capable of physiological monitoring. Such a metric could be used to help manage noncritical in-vehicle information systems and could also provide a continuous measure of how different road and traffic conditions affect drivers.
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paper_title: A Brain-Computer Interface for classifying EEG correlates of chronic mental stress

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In this paper, a Brain-Computer Interface (BCI) for classifying EEG correlates of chronic mental stress is proposed. Data from 8 EEG channels are collected from 26 healthy right-handed students during university examination period and after the examination whereby the former is considered to be relatively more stressful to students than the latter. The mental stress level are measured using the Perceived Stress Scale 14 (PSS-14) and categorized into stressed and stress-free groups. The proposed BCI is then used to classify the subjects' mental stress level on EEG features extracted using the Higuchi's fractal dimension of EEG, Gaussian mixtures of EEG spectrogram, and Magnitude Square Coherence Estimation (MSCE) between the EEG channels. Classification on the EEG features are then performed using the K-Nearest Neighbor (K-NN) and Support Vector Machine (SVM). The performance of the proposed BCI is then evaluated from the inter-subject classification accuracy using leave-one-out validation. The results showed that the proposed BCI using features extracted by MSCE yielded a promising inter-subject validation accuracy of over 90% in classifying the EEG correlates of chronic mental stress.
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paper_title: A wearable, EEG-based massage headband for anxiety alleviation

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In this work, we would like to discuss our findings obtained from the newly proposed hardware design for anxiety detection and its mitigation where a subject's state of mind is identified from the neurological data retrieved. A feedback-based network implemented for stress alleviation mainly comprises of a pair of massage motors and RGB light-emitting diode (LED) that activate during conditions of stress detected, a custom-made Electroencephalography (EEG)-sensor and a massage motor circuit both functioning on an Arduino driven platform. The skin electrodes facilitate a hassle-free retrieval of beta waves from the frontal areas that are transmitted wirelessly by a Bluetooth console to a computer post signal amplification and filtration. Rising amplitudes of beta signals that are associated to anxiety have been successfully tackled in three out of four subjects by suppressing the high values due to the massage motor therapy introduced. The motors on sensing high amplitude values exceeding the pre-set threshold limits during the three experiment trials rotate smoothly thus helping one to relax and guaranteeing a higher work performance.
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paper_title: Cloud-Based or On-Device: An Empirical Study of Mobile Deep Inference

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Modern mobile applications are benefiting significantly from the advancement in deep learning, e.g., implementing real-time image recognition and conversational system. Given a trained deep learning model, applications usually need to perform a series of matrix operations based on the input data, in order to infer possible output values. Because of computational complexity and size constraints, these trained models are often hosted in the cloud. To utilize these cloud-based models, mobile apps will have to send input data over the network. While cloud-based deep learning can provide reasonable response time for mobile apps, it restricts the use case scenarios, e.g. mobile apps need to have network access. With mobile specific deep learning optimizations, it is now possible to employ on-device inference. However, because mobile hardware, such as GPU and memory size, can be very limited when compared to its desktop counterpart, it is important to understand the feasibility of this new on-device deep learning inference architecture. In this paper, we empirically evaluate the inference performance of three Convolutional Neural Networks (CNNs) using a benchmark Android application we developed. Our measurement and analysis suggest that on-device inference can cost up to two orders of magnitude greater response time and energy when compared to cloud-based inference, and that loading model and computing probability are two performance bottlenecks for on-device deep inferences.
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paper_title: Self-Monitoring of Emotions and Mood Using a Tangible Approach

paper_content:

Nowadays Personal Informatics (PI) devices are used for sensing and saving personal data, everywhere and at any time, helping people improve their lives by highlighting areas of good and bad performances and providing a general awareness of different levels of conduct. However, not all these data are suitable to be automatically collected. This is especially true for emotions and mood. Moreover, users without experience in self-tracking may have a misperception of PI applications’ limits and potentialities. We believe that current PI tools are not designed with enough understanding of such users’ needs, desires, and problems they may encounter in their everyday lives. We designed and prototype the Mood TUI (Tangible User Interface), a PI tool that supports the self-reporting of mood data using a tangible interface. The platform is able to gather six different mood states and it was tested through several participatory design sessions in a secondary/high school. The solution proposed allows gathering mood values in an amusing, simple, and appealing way. Users appreciated the prototypes, suggesting several possible improvements as well as ideas on how to use the prototype in similar or totally different contexts, and giving us hints for future research.
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paper_title: Tangible Drops: A Visio-Tactile Display Using Actuated Liquid-Metal Droplets

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We present Tangible Drops, a visio-tactile display that for the first time provides physical visualization and tactile feedback using a planar liquid interface. It presents digital information interactively by tracing dynamic patterns on horizontal flat surfaces using liquid metal drops on a programmable electrode array. It provides tactile feedback with directional information in the 2D vector plane using linear locomotion and/or vibration of the liquid metal drops. We demonstrate move, oscillate, merge, split and dispense-from-reservoir functions of the liquid metal drops by consuming low power (450 mW per electrode) and low voltage (8--15 V). We report on results of our empirical study with 12 participants on tactile feedback using 8 mm diameter drops, which indicate that Tangible Drops can convey tactile sensations such as changing speed, varying direction and controlled oscillation with no visual feedback. We present the design space and demonstrate the applications of Tangible Drops, and conclude by suggesting potential future applications for the technique.
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Title: Beyond Mobile Apps: A Survey of Technologies for Mental Well-being
Section 1: INTRODUCTION
Description 1: Introduces the global challenge of mental health, traditional assessments, and the emergence of technological advancements for mental well-being.

Section 2: Traditional Assessment tools and techniques
Description 2: Details traditional methods to assess mental well-being, including self-reporting, standardized clinical questionnaires, and diagnostic interviews.

Section 3: Technological supplements to traditional assessment techniques
Description 3: Discusses advancements in technology that supplement traditional mental health assessments, such as mobile apps and tangible interfaces.

Section 4: Overview of mHealth apps
Description 4: Reviews the prevalence, functionality, and challenges associated with mobile health apps designed for mental well-being.

Section 5: Tangible interfaces
Description 5: Explores the use of tangible user interfaces in promoting mental health, including their benefits and current challenges.

Section 6: Sensing mental well-being state using mHealth apps
Description 6: Examines the potential of mobile health apps to infer mental well-being in real-time by utilizing sensors and machine learning.

Section 7: Tools to promote positive behaviour change
Description 7: Investigates technologies aimed at promoting positive behavior changes, including virtual and augmented reality, and biofeedback therapy.

Section 8: Real-time tangible feedback interfaces
Description 8: Looks into technologies that combine sensing and feedback mechanisms to improve mental well-being in real time.

Section 9: REFLECTION AND CHALLENGES OF MENTAL HEALTH TECHNOLOGIES
Description 9: Reflects on the opportunities and challenges posed by mental health technologies based on the survey's findings.

Section 10: Discussion of existing research
Description 10: Analyzes the survey findings on apps, sensors, interfaces, robotics, and biofeedback for supporting mental well-being.

Section 11: Challenges
Description 11: Addresses the various challenges associated with developing and implementing mental well-being technologies.

Section 12: User feedback
Description 12: Reports user feedback from focus groups on mental well-being technologies, highlighting preferences and potential improvements.

Section 13: Advancements to enable real-time intervention
Description 13: Discusses recent technological advancements that support the development of real-time intervention tools for mental well-being.

Section 14: CONCLUSION
Description 14: Summarizes the potential and challenges of various methods to sense and improve mental well-being using technology.