Privacy implications of accelerometer data: a review of possible inferences
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paper_title: SemaDroid: A Privacy-Aware Sensor Management Framework for Smartphones

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While mobile sensing applications are booming, the sensor management mechanisms in current smartphone operating systems are left behind -- they are incomprehensive and coarse-grained, exposing a huge attack surface for malicious or aggressive third party apps to steal user's private information through mobile sensors. In this paper, we propose a privacy-aware sensor management framework, called SemaDroid, which extends the existing sensor management framework on Android to provide comprehensive and fine-grained access control over onboard sensors. SemaDroid allows the user to monitor the sensor usage of installed apps, and to control the disclosure of sensing information while not affecting the app's usability. Furthermore, SemaDroid supports context-aware and quality-of-sensing based access control policies. The enforcement and update of the policies are in real-time. Detailed design and implementation of SemaDroid on Android are presented to show that SemaDroid works compatible with the existing Android security framework. Demonstrations are also given to show the capability of SemaDroid on sensor management and on defeating emerging sensor-based attacks. Finally, we show the high efficiency and security of SemaDroid.
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paper_title: Exploring privacy concerns about personal sensing

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More and more personal devices such as mobile phones and multimedia players use embedded sensing. This means that people are wearing and carrying devices capable of sensing details about them such as their activity, location, and environment. In this paper, we explore privacy concerns about such personal sensing through interviews with 24 participants who took part in a three month study that used personal sensing to detect their physical activities. Our results show that concerns often depended on what was being recorded, the context in which participants worked and lived and thus would be sensed, and the value they perceived would be provided. We suggest ways in which personal sensing can be made more privacy-sensitive to address these concerns.
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paper_title: Mobile Device Identification via Sensor Fingerprinting

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We demonstrate how the multitude of sensors on a smartphone can be used to construct a reliable hardware fingerprint of the phone. Such a fingerprint can be used to de-anonymize mobile devices as they connect to web sites, and as a second factor in identifying legitimate users to a remote server. We present two implementations: one based on analyzing the frequency response of the speakerphone-microphone system, and another based on analyzing device-specific accelerometer calibration errors. Our accelerometer-based fingerprint is especially interesting because the accelerometer is accessible via JavaScript running in a mobile web browser without requesting any permissions or notifying the user. We present the results of the most extensive sensor fingerprinting experiment done to date, which measured sensor properties from over 10,000 mobile devices. We show that the entropy from sensor fingerprinting is sufficient to uniquely identify a device among thousands of devices, with low probability of collision.
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paper_title: Sensor Guardian: prevent privacy inference on Android sensors

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Privacy inference attacks based on sensor data is an emerging and severe threat on smart devices, in which malicious applications leverage data from innocuous sensors to infer sensitive information of user, e.g., utilizing accelerometers to infer user’s keystroke. In this paper, we present Sensor Guardian, a privacy protection system that mitigates this threat on Android by hooking and controlling applications’ access to sensors. Sensor Guardian inserts hooks into applications by statically instrumenting their APK (short for Android Package Kit) files and enforces control policies in these hooks at runtime. Our evaluation shows that Sensor Guardian can effectively and efficiently mitigate the privacy inference threat on Android sensors, with negligible overhead during both static instrumentation and runtime control.
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paper_title: Doorjamb: unobtrusive room-level tracking of people in homes using doorway sensors

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Indoor tracking systems will be an essential part of the home of the future, enabling location-aware and individually-tailored services. However, today there are no tracking solutions that are practical for "every day" use in the home. In this paper, we introduce the Doorjamb tracking system that uses ultrasonic range finders mounted above each doorway, pointed downward to sense people as they walk through the doorway. The system differentiates people by measuring their heights, infers their walking direction using signal processing, and identifies their room locations based on the sequence of doorways through which they pass. Doorjamb provides room-level tracking without requiring any user participation, wearable devices, privacy-intrusive sensors, or high-cost sensors. We create a proof-of-concept implementation and empirically evaluate Doorjamb with experiments that include over 3000 manually-recorded doorway crossings. Results indicate that the system can perform room-level tracking with 90% accuracy on average.
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paper_title: Actitracker: A Smartphone-Based Activity Recognition System for Improving Health and Well-Being

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Actitracker is a smartphone-based activity-monitoring service to help people ensure they receive sufficient activity to maintain proper health. This free service allowed people to set personal activity goals and monitor their progress toward these goals. Actitracker uses machine learning methods to recognize a user's activities. It initially employs a "universal" model generated from labeled activity data from a panel of users, but will automatically shift to a much more accurate personalized model once a user completes a simple training phase. Detailed activity reports and statistics are maintained and provided to the user. Actitracker is a research-based system that began in 2011, before fitness trackers like Fitbit were popular, and was deployed for public use from 2012 until 2015, during which period it had 1,000 registered users. This paper describes the Actitracker system, its use of machine learning, and user experiences. While activity recognition has now entered the mainstream, this paper provides insights into applied activity recognition, something that commercial companies rarely share.
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paper_title: uWave: Accelerometer-based personalized gesture recognition and its applications

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The proliferation of accelerometers on consumer electronics has brought an opportunity for interaction based on gestures or physical manipulation of the devices. We present uWave, an efficient recognition algorithm for such interaction using a single three-axis accelerometer. Unlike statistical methods, uWave requires a single training sample for each gesture pattern and allows users to employ personalized gestures and physical manipulations. We evaluate uWave using a large gesture library with over 4000 samples collected from eight users over an elongated period of time for a gesture vocabulary with eight gesture patterns identified by a Nokia research. It shows that uWave achieves 98.6% accuracy, competitive with statistical methods that require significantly more training samples. Our evaluation data set is the largest and most extensive in published studies, to the best of our knowledge. We also present applications of uWave in gesture-based user authentication and interaction with three-dimensional mobile user interfaces using user created gestures.
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paper_title: Detection of Activities by Wireless Sensors for Daily Life Surveillance: Eating and Drinking

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This paper introduces a two-stage approach to the detection of people eating and/or drinking for the purposes of surveillance of daily life. With the sole use of wearable accelerometer sensor attached to somebody’s (man or a woman) wrists, this two-stage approach consists of feature extraction followed by classification. At the first stage, based on the limb’s three dimensional kinematics movement model and the Extended Kalman Filter (EKF), the realtime arm movement features described by Euler angles are extracted from the raw accelerometer measurement data. In the latter stage, the Hierarchical Temporal Memory (HTM) network is adopted to classify the extracted features of the eating/drinking activities based on the space and time varying property of the features, by making use of the powerful modelling capability of HTM network on dynamic signals which is varying with both space and time. The proposed approach is tested through the real eating and drinking activities using the three dimensional accelerometers. Experimental results show that the EKF and HTM based two-stage approach can perform the activity detection successfully with very high accuracy.
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paper_title: Driving Behavior and Traffic Safety: An Acceleration-Based Safety Evaluation Procedure for Smartphones

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Traffic safety and energy efficiency of vehicles are strictly related to driver’s behavior. The scientific literature has investigated on some specific dynamic parameters that, among the others, can be used as a measure of unsafe or aggressive driving style such as longitudinal and lateral acceleration of vehicle. Moreover, the use of modern mobile devices (smartphones and tablets), and their internal sensors (GPS receivers, three-axes accelerometers), allows road users to receive real time information and feedback that can be useful to increase awareness of drivers and promote safety. This paper focuses on the development of a prototype mobile application that can evaluate the grade of safety that drivers are keeping on the road by measuring of accelerations (longitudinal and lateral) and warning for users when it can be convenient to correct their driving style. The aggressiveness is evaluated by plotting vehicle’s acceleration on a g-g diagram specially studied and designed, where horizontal and lateral acceleration is displayed inside areas of “Good Driving Style”. Several experimental tests were carried out with different drivers and cars in order to estimate the system accuracy and the usability of the application. This work is part of the wider research project M2M, Mobile to Mobility: Information and communication technology systems for road traffic safety (PON National Operational Program for Research and Competitiveness 2007-2013) which is based on the use of mobile sensor computing systems for giving real-time information in order to reduce risks and to make the transportation system more safe and comfortable.
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paper_title: The Validity of a New Consumer-Targeted Wrist Device in Sleep Measurement: An Overnight Comparison Against Polysomnography in Children and Adolescents

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STUDY OBJECTIVES ::: The validity of consumer-targeted wrist-worn sleep measurement systems has been little studied in children and adolescents. We examined the validity of a new fitness tracker (PFT) manufactured by Polar Electro Oy and the previously validated Actiwatch 2 (AW2) from Philips Respironics against polysomnography (PSG) in children and adolescents. ::: ::: ::: METHODS ::: Seventeen children (age 11.0 ± 0.8 years) and 17 adolescents (age 17.8 ± 1.8 years) wore the PFT and AW2 concurrently with an ambulatory PSG in their own home for 1 night. We compared sleep onset, offset, sleep interval (time from sleep on to offset), actual sleep time (time scored as sleep between sleep on and offset), and wake after sleep onset (WASO) between accelerometers and PSG. Sensitivity, specificity, and accuracy were calculated from the epoch-by-epoch data. ::: ::: ::: RESULTS ::: Both devices performed adequately against PSG, with excellent sensitivity for both age groups (> 0.91). In terms of specificity, the PFT was adequate in both groups (> 0.77), and AW2 adequate in children (0.68) and poor in adolescents (0.58). In the younger group, the PFT underestimated actual sleep time by 29.9 minutes and AW2 underestimated actual sleep time by 43.6 minutes. Both overestimated WASO, PFT by 24.4 minutes and AW2 by 20.9 minutes. In the older group, both devices underestimated actual sleep time (PFT by 20.6 minutes and AW2 by 26.8 minutes) and overestimated WASO (PFT by 12.5 minutes and AW2 by 14.3 minutes). Both devices were accurate in defining sleep onset. ::: ::: ::: CONCLUSIONS ::: This study suggests that this consumer-targeted wrist-worn device performs as well as, or even better than, the previously validated AW2 against PSG in children and adolescents. Both devices underestimated sleep but to a lesser extent than seen in many previous validation studies on research-targeted accelerometers.
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paper_title: Using mobile phone sensors to detect driving behavior

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In India, an increasing number of vehicles on the roads, in recent past, have led to an increase in the number of road accidents. There have been alarming statistics regarding the number of accidents per day in India. At least 1,42,000 people died due to road accidents in India in the year 2011. Bad driving, lax traffic control, and poor road conditions are the main reason for this. In this work, we present a mobile phone application that uses combination of in-built sensors, GPS, micro-phone and accelerometer, to detect driving behavior along with road and traffic conditions. This application will prove helpful in detecting bad driving as well as road and traffic conditions in order to assist a willing individual to change his or her driving behavior. The law and enforcement agencies may also use this data in analyzing the ground realities for increasing number of road accidents.
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paper_title: Estimating load carriage from a body-worn accelerometer

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Heavy loads increase the risk of musculoskeletal injury for foot soldiers and first responders. Continuous monitoring of load carriage in the field has proven difficult. We propose an algorithm for estimating load from a single body-worn accelerometer. The algorithm utilizes three different methods for characterizing torso movement dynamics, and maps the extracted dynamics features to load estimates using two machine learning multivariate regression techniques. The algorithm is applied, using leave-one-subject-out cross-validation, to two field collections of soldiers and civilians walking with varying loads. Rapid, accurate estimates of load are obtained, demonstrating robustness to changes in equipment configuration, walking conditions, and walking speeds. On soldier data with loads ranging from 45 to 89 lbs, load estimates result in mean absolute error (MAE) of 6.64 lbs and correlation of r = 0.81. On combined soldier and civilian data, with loads ranging from 0 to 89 lbs, results are MAE = 9.57 lbs and r = 0.91.
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paper_title: Smart Elderly Home Monitoring System with an Android Phone

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Falls, heart attack and stroke are among the leading causes of hospitalization for the elderly and illness individual. The chances of surviving a fall, heart attack or stroke are much greater if the senior gets help within an hour. In this project, a smart elderly home monitoring system (SEHMS) is designed and developed. An Android-based smart phone with 3-axial accelerometer is used as the telehealth device which could detect a fall of the carrier. The smart phone is then connected to the monitoring system by using the TCP/IP networking method via Wi-Fi. A graphical user interface (GUI) is developed as the monitoring system which exhibits the information gathered from the system. In addition, the concept of a remote panic button has been tested and implemented in this project by using the same android based smart phone. With the developed system, elderly and chronically ill patients could stay independently in their own home with care facilities and secure in the knowledge that they are being monitored.
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paper_title: Activity classification using a single wrist-worn accelerometer

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Automatic identification of human activity has led to a possibility of providing personalised services in different domains i.e. healthcare, security and sport etc. With advancement in sensor technology, automatic activity recognition can be done in an unobtrusive and non-intrusive way. The placement of the sensor and wearability are ones of vital keys in the successful activity recognition of free space livings. Experiments were carried out to investigate the use of a single wrist-worn accelerometer for automatic activity classification. The performances of two classification algorithms namely Decision Tree C4.5 and Artificial Neural Network were compared using four different sets of features to classify five daily living activities. The result revealed that Decision Tree C4.5 has outperformed Neural Network regardless of the different sets of features used. The best classification result was achieved using the set containing the most popular and accurate features i.e. mean, minimum, energy and sample differences etc. The best accuracy of 94.13% was achieved using only wrist-worn accelerometer showing a possibility of automatic activity classification with no movement constrain, discomfort and stigmatisation caused by the sensor.
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paper_title: Feature Selection and Activity Recognition System Using a Single Triaxial Accelerometer

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Activity recognition is required in various applications such as medical monitoring and rehabilitation. Previously developed activity recognition systems utilizing triaxial accelerometers have provided mixed results, with subject-to-subject variability. This paper presents an accurate activity recognition system utilizing a body worn wireless accelerometer, to be used in the real-life application of patient monitoring. The algorithm utilizes data from a single, waist-mounted triaxial accelerometer to classify gait events into six daily living activities and transitional events. The accelerometer can be worn at any location around the circumference of the waist, thereby reducing user training. Feature selection is performed using Relief-F and sequential forward floating search (SFFS) from a range of previously published features, as well as new features introduced in this paper. Relevant and robust features that are insensitive to the positioning of accelerometer around the waist are selected. SFFS selected almost half the number of features in comparison to Relief-F and provided higher accuracy than Relief-F. Activity classification is performed using Naive Bayes and $k$ -nearest neighbor $(k$ -NN) and the results are compared. Activity recognition results on seven subjects with leave-one-person-out error estimates show an overall accuracy of about 98% for both the classifiers. Accuracy for each of the individual activity is also more than 95%.
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paper_title: Mobile phone based drunk driving detection

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Drunk driving, or officially Driving Under the Influence (DUI) of alcohol, is a major cause of traffic accidents throughout the world. In this paper, we propose a highly efficient system aimed at early detection and alert of dangerous vehicle maneuvers typically related to drunk driving. The entire solution requires only a mobile phone placed in vehicle and with accelerometer and orientation sensor. A program installed on the mobile phone computes accelerations based on sensor readings, and compares them with typical drunk driving patterns extracted from real driving tests. Once any evidence of drunk driving is present, the mobile phone will automatically alert the driver or call the police for help well before accident actually happens. We implement the detection system on Android G1 phone and have it tested with different kinds of driving behaviors. The results show that the system achieves high accuracy and energy efficiency.
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paper_title: Physical activity monitoring by use of accelerometer-based body-worn sensors in older adults: A systematic literature review of current knowledge and applications

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Abstract Objectives To systematically review the literature on physical activity variables derived from body-worn sensors during long term monitoring in healthy and in-care older adults. Methods Using pre-designed inclusion and exclusion criteria, a PubMed search strategy was designed to trace relevant reports of studies. Last search date was March 8, 2011. Study selection Studies that included persons with mean or median age of >65 years, used accelerometer-based body-worn sensors with a monitoring length of >24 h, and reported values on physical activity in the samples assessed. Results 1403 abstracts were revealed and 134 full-text papers included in the final review. A variety of variables derived from activity counts or recognition of performed activities were reported in healthy older adults as well as in in-care older adults. Three variables were possible to compare across studies, level of Energy Expenditure in kcal per day and activity recognition in terms of total time in walking and total activity. However, physical activity measured by these variables demonstrated large variation between studies and did not distinguish activity between healthy and in-care samples. Conclusion There is a rich variety in methods used for data collection and analysis as well as in reported variables. Different aspects of physical activity can be described, but the variety makes it challenging to compare across studies. There is an urgent need for developing consensus on activity monitoring protocols and which variables to report.
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paper_title: Accelerometer Data Collection and Processing Criteria to Assess Physical Activity and Other Outcomes: A Systematic Review and Practical Considerations

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BACKGROUND ::: Accelerometers are widely used to measure sedentary time, physical activity, physical activity energy expenditure (PAEE), and sleep-related behaviors, with the ActiGraph being the most frequently used brand by researchers. However, data collection and processing criteria have evolved in a myriad of ways out of the need to answer unique research questions; as a result there is no consensus. ::: ::: ::: OBJECTIVES ::: The purpose of this review was to: (1) compile and classify existing studies assessing sedentary time, physical activity, energy expenditure, or sleep using the ActiGraph GT3X/+ through data collection and processing criteria to improve data comparability and (2) review data collection and processing criteria when using GT3X/+ and provide age-specific practical considerations based on the validation/calibration studies identified. ::: ::: ::: METHODS ::: Two independent researchers conducted the search in PubMed and Web of Science. We included all original studies in which the GT3X/+ was used in laboratory, controlled, or free-living conditions published from 1 January 2010 to the 31 December 2015. ::: ::: ::: RESULTS ::: The present systematic review provides key information about the following data collection and processing criteria: placement, sampling frequency, filter, epoch length, non-wear-time, what constitutes a valid day and a valid week, cut-points for sedentary time and physical activity intensity classification, and algorithms to estimate PAEE and sleep-related behaviors. The information is organized by age group, since criteria are usually age-specific. ::: ::: ::: CONCLUSION ::: This review will help researchers and practitioners to make better decisions before (i.e., device placement and sampling frequency) and after (i.e., data processing criteria) data collection using the GT3X/+ accelerometer, in order to obtain more valid and comparable data. ::: ::: ::: PROSPERO REGISTRATION NUMBER ::: CRD42016039991.
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paper_title: Mining for motivation: using a single wearable accelerometer to detect people's interests

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This paper presents a novel investigation of how motion as measured with just a single wearable accelerometer is informative of people's interests and motivation during crowded social events. We collected accelerometer readings on a large number of people (32 and 46 people in two crowded social events involving up to hundreds of people). In our experiments, we demonstrate how people's movements are informative of their particular interests: during talks, their interests in particular topics, and during networking events, their interest to participate successfully to make new contacts and foster existing ones. To our knowledge, using a single body worn accelerometer to measure and automatically infer these aspects of social behaviour has never been attempted before. Our experiments show that despite the challenge of the proposed task, useful automated predictions are possible and demonstrate the potential for further research in this area.
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paper_title: Activity Recognition Using a Single Accelerometer Placed at the Wrist or Ankle

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AB Purpose: Large physical activity surveillance projects such as the UK Biobank and NHANES are using wrist-worn accelerometer-based activity monitors that collect raw data. The goal is to increase wear time by asking subjects to wear the monitors on the wrist instead of the hip, and then to use information in the raw signal to improve activity type and intensity estimation. The purposes of this work was to obtain an algorithm to process wrist and ankle raw data and to classify behavior into four broad activity classes: ambulation, cycling, sedentary, and other activities. Methods: Participants (N = 33) wearing accelerometers on the wrist and ankle performed 26 daily activities. The accelerometer data were collected, cleaned, and preprocessed to extract features that characterize 2-, 4-, and 12.8-s data windows. Feature vectors encoding information about frequency and intensity of motion extracted from analysis of the raw signal were used with a support vector machine classifier to identify a subject's activity. Results were compared with categories classified by a human observer. Algorithms were validated using a leave-one-subject-out strategy. The computational complexity of each processing step was also evaluated. Results: With 12.8-s windows, the proposed strategy showed high classification accuracies for ankle data (95.0%) that decreased to 84.7% for wrist data. Shorter (4 s) windows only minimally decreased performances of the algorithm on the wrist to 84.2%. Conclusions: A classification algorithm using 13 features shows good classification into the four classes given the complexity of the activities in the original data set. The algorithm is computationally efficient and could be implemented in real time on mobile devices with only 4-s latency.
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paper_title: Validity of 10 electronic pedometers for measuring steps, distance, and energy cost.

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ABSTRACTCROUTER, S. E., P. L. SCHNEIDER, M. KARABULUT, and D. R. BASSETT, JR. Validity of 10 Electronic Pedometers for Measuring Steps, Distance, and Energy Cost. Med. Sci. Sports Exerc., Vol. 35, No. 8, pp. 1455–1460, 2003.PurposeThis study examined the effects of walking speed on the accuracy and
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paper_title: puffMarker: a multi-sensor approach for pinpointing the timing of first lapse in smoking cessation

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Recent researches have demonstrated the feasibility of detecting smoking from wearable sensors, but their performance on real-life smoking lapse detection is unknown. In this paper, we propose a new model and evaluate its performance on 61 newly abstinent smokers for detecting a first lapse. We use two wearable sensors --- breathing pattern from respiration and arm movements from 6-axis inertial sensors worn on wrists. In 10-fold cross-validation on 40 hours of training data from 6 daily smokers, our model achieves a recall rate of 96.9%, for a false positive rate of 1.1%. When our model is applied to 3 days of post-quit data from 32 lapsers, it correctly pinpoints the timing of first lapse in 28 participants. Only 2 false episodes are detected on 20 abstinent days of these participants. When tested on 84 abstinent days from 28 abstainers, the false episode per day is limited to 1/6.
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paper_title: A Triaxial Accelerometer-Based Physical-Activity Recognition via Augmented-Signal Features and a Hierarchical Recognizer

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Physical-activity recognition via wearable sensors can provide valuable information regarding an individual's degree of functional ability and lifestyle. In this paper, we present an accelerometer sensor-based approach for human-activity recognition. Our proposed recognition method uses a hierarchical scheme. At the lower level, the state to which an activity belongs, i.e., static, transition, or dynamic, is recognized by means of statistical signal features and artificial-neural nets (ANNs). The upper level recognition uses the autoregressive (AR) modeling of the acceleration signals, thus, incorporating the derived AR-coefficients along with the signal-magnitude area and tilt angle to form an augmented-feature vector. The resulting feature vector is further processed by the linear-discriminant analysis and ANNs to recognize a particular human activity. Our proposed activity-recognition method recognizes three states and 15 activities with an average accuracy of 97.9% using only a single triaxial accelerometer attached to the subject's chest.
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paper_title: ACComplice: Location inference using accelerometers on smartphones

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The security and privacy risks posed by smartphone sensors such as microphones and cameras have been well documented. However, the importance of accelerometers have been largely ignored. We show that accelerometer readings can be used to infer the trajectory and starting point of an individual who is driving. This raises concerns for two main reasons. First, unauthorized access to an individual's location is a serious invasion of privacy and security. Second, current smartphone operating systems allow any application to observe accelerometer readings without requiring special privileges. We demonstrate that accelerometers can be used to locate a device owner to within a 200 meter radius of the true location. Our results are comparable to the typical accuracy for handheld global positioning systems.
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paper_title: We Can Track You if You Take the Metro: Tracking Metro Riders Using Accelerometers on Smartphones

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Motion sensors, especially accelerometers, on smartphones have been discovered to be a powerful side channel for spying on users’ privacy. In this paper, we reveal a new accelerometer-based side-channel attack which is particularly serious: malware on smartphones can easily exploit the accelerometers to trace metro riders stealthily. We first address the challenge to automatically filter out metro-related data from a mass of miscellaneous accelerometer readings, and then propose a basic attack which leverages an ensemble interval classifier built from supervised learning to infer the riding trajectory of the user. As the supervised learning requires the attacker to collect labeled training data for each station interval, this attack confronts the scalability problem in big cities with a huge metro network. We thus further present an improved attack using semi-supervised learning, which only requires the attacker to collect labeled data for a very small number of distinctive station intervals. We conduct real experiments on a large self-built dataset, which contains more than 120 h of data collected from six metro lines of three major cities. The results show that the inferring accuracy could reach 89% and 94% if the user takes the metro for four and six stations, respectively. We finally discuss possible countermeasures against the proposed attack.
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paper_title: On the Anonymity of Home/Work Location Pairs

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Many applications benefit from user location data, but location data raises privacy concerns. Anonymization can protect privacy, but identities can sometimes be inferred from supposedly anonymous data. This paper studies a new attack on the anonymity of location data. We show that if the approximate locations of an individual's home and workplace can both be deduced from a location trace, then the median size of the individual's anonymity set in the U.S. working population is 1, 21 and 34,980, for locations known at the granularity of a census block, census track and county respectively. The location data of people who live and work in different regions can be re-identified even more easily. Our results show that the threat of re-identification for location data is much greater when the individual's home and work locations can both be deduced from the data. To preserve anonymity, we offer guidance for obfuscating location traces before they are disclosed.
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paper_title: Mobile Device Identification via Sensor Fingerprinting

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We demonstrate how the multitude of sensors on a smartphone can be used to construct a reliable hardware fingerprint of the phone. Such a fingerprint can be used to de-anonymize mobile devices as they connect to web sites, and as a second factor in identifying legitimate users to a remote server. We present two implementations: one based on analyzing the frequency response of the speakerphone-microphone system, and another based on analyzing device-specific accelerometer calibration errors. Our accelerometer-based fingerprint is especially interesting because the accelerometer is accessible via JavaScript running in a mobile web browser without requesting any permissions or notifying the user. We present the results of the most extensive sensor fingerprinting experiment done to date, which measured sensor properties from over 10,000 mobile devices. We show that the entropy from sensor fingerprinting is sufficient to uniquely identify a device among thousands of devices, with low probability of collision.
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paper_title: Whose move is it anyway? Authenticating smart wearable devices using unique head movement patterns

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In this paper, we present the design, implementation and evaluation of a user authentication system, Headbanger, for smart head-worn devices, through monitoring the user's unique head-movement patterns in response to an external audio stimulus. Compared to today's solutions, which primarily rely on indirect authentication mechanisms via the user's smartphone, thus cumbersome and susceptible to adversary intrusions, the proposed head-movement based authentication provides an accurate, robust, light-weight and convenient solution. Through extensive experimental evaluation with 95 participants, we show that our mechanism can accurately authenticate users with an average true acceptance rate of 95.57% while keeping the average false acceptance rate of 4.43%. We also show that even simple head-movement patterns are robust against imitation attacks. Finally, we demonstrate our authentication algorithm is rather light-weight: the overall processing latency on Google Glass is around 1.9 seconds.
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paper_title: Authentication of Smartphone Users Based on the Way They Walk Using k-NN Algorithm

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Accelerometer-based biometric gait recognition offers a convenient way to authenticate users on their mobile devices. Modern smartphones contain in-built accelerometers which can be used as sensors to acquire the necessary data while the subjects are walking. Hence, no additional costs for special sensors are imposed to the user. In this publication we extract several features from the gait data and use the k-Nearest Neighbour algorithm for classification. We show that this algorithm yields a better biometric performance than the machine learning algorithms we previously used for classification, namely Hidden Markov Models and Support Vector Machines. We implemented the presented method on a smartphone and demonstrate that it is efficient enough to be applied in practice.
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paper_title: Context-Aware Active Authentication Using Smartphone Accelerometer Measurements

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While body movement patterns recorded by a smartphone accelerometer are now well understood to be discriminative enough to separate users, little work has been done to address the question of if or how the position in which the phone is held affects user authentication. In this work, we show through a combination of supervised learning methods and statistical tests, that there are certain users for whom exploitation of information of how a phone is held drastically improves classification performance. We propose a two-stage authentication framework that identifies the location of the phone before performing authentication, and show its benefits based on a dataset of 30 users. Our work represents a first step towards bridging the gap between accelerometer-based authentication systems analyzed from the context of a laboratory environment and a real accelerometer-based authentication system in the wild where phone positioning cannot be assumed.
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paper_title: Evaluating the Privacy Risk of Location-Based Services

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In modern mobile networks, users increasingly share their location with third-parties in return for location-based services. Previous works show that operators of location-based services may identify users based on the shared location information even if users make use of pseudonyms. In this paper, we push the understanding of the privacy risk further. We evaluate the ability of location-based services to identify users and their points of interests based on different sets of location information. We consider real life scenarios of users sharing location information with location-based services and quantify the privacy risk by experimenting with real-world mobility traces.
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paper_title: Cell phone-based biometric identification

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Mobile devices are becoming increasingly sophisticated and now incorporate many diverse and powerful sensors. The latest generation of smart phones is especially laden with sensors, including GPS sensors, vision sensors (cameras), audio sensors (microphones), light sensors, temperature sensors, direction sensors (compasses), and acceleration sensors. In this paper we describe and evaluate a system that uses phone-based acceleration sensors, called accelerometers, to identify and authenticate cell phone users. This form of behavioral biometrie identification is possible because a person's movements form a unique signature and this is reflected in the accelerometer data that they generate. To implement our system we collected accelerometer data from thirty-six users as they performed normal daily activities such as walking, jogging, and climbing stairs, aggregated this time series data into examples, and then applied standard classification algorithms to the resulting data to generate predictive models. These models either predict the identity of the individual from the set of thirty-six users, a task we call user identification, or predict whether (or not) the user is a specific user, a task we call user authentication. This work is notable because it enables identification and authentication to occur unobtrusively, without the users taking any extra actions-all they need to do is carry their cell phones. There are many uses for this work. For example, in environments where sharing may take place, our work can be used to automatically customize a mobile device to a user. It can also be used to provide device security by enabling usage for only specific users and can provide an extra level of identity verification.
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paper_title: TouchLogger: Inferring keystrokes on touch screen from smartphone motion

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Attacks that use side channels, such as sound and electromagnetic emanation, to infer keystrokes on physical keyboards are ineffective on smartphones without physical keyboards. We describe a new side channel, motion, on touch screen smartphones with only soft keyboards. Since typing on different locations on the screen causes different vibrations, motion data can be used to infer the keys being typed. To demonstrate this attack, we developed TouchLogger, an Android application that extracts features from device orientation data to infer keystrokes. TouchLogger correctly inferred more than 70% of the keys typed on a number-only soft keyboard on a smartphone. We hope to raise the awareness of motion as a significant side channel that may leak confidential data.
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paper_title: Deep-Spying: Spying using Smartwatch and Deep Learning

paper_content:

Wearable technologies are today on the rise, becoming more common and broadly available to mainstream users. In fact, wristband and armband devices such as smartwatches and fitness trackers already took an important place in the consumer electronics market and are becoming ubiquitous. By their very nature of being wearable, these devices, however, provide a new pervasive attack surface threatening users privacy, among others. ::: In the meantime, advances in machine learning are providing unprecedented possibilities to process complex data efficiently. Allowing patterns to emerge from high dimensional unavoidably noisy data. ::: The goal of this work is to raise awareness about the potential risks related to motion sensors built-in wearable devices and to demonstrate abuse opportunities leveraged by advanced neural network architectures. ::: The LSTM-based implementation presented in this research can perform touchlogging and keylogging on 12-keys keypads with above-average accuracy even when confronted with raw unprocessed data. Thus demonstrating that deep neural networks are capable of making keystroke inference attacks based on motion sensors easier to achieve by removing the need for non-trivial pre-processing pipelines and carefully engineered feature extraction strategies. Our results suggest that the complete technological ecosystem of a user can be compromised when a wearable wristband device is worn.
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paper_title: TapLogger: inferring user inputs on smartphone touchscreens using on-board motion sensors

paper_content:

Today's smartphones are shipped with various embedded motion sensors, such as the accelerometer, gyroscope, and orientation sensors. These motion sensors are useful in supporting the mobile UI innovation and motion-based commands. However, they also bring potential risks of leaking user's private information as they allow third party applications to monitor the motion changes of smartphones. In this paper, we study the feasibility of inferring a user's tap inputs to a smartphone with its integrated motion sensors. Specifically, we utilize an installed trojan application to stealthily monitor the movement and gesture changes of a smartphone using its on-board motion sensors. When the user is interacting with the trojan application, it learns the motion change patterns of tap events. Later, when the user is performing sensitive inputs, such as entering passwords on the touchscreen, the trojan application applies the learnt pattern to infer the occurrence of tap events on the touchscreen as well as the tapped positions on the touchscreen. For demonstration, we present the design and implementation of TapLogger, a trojan application for the Android platform, which stealthily logs the password of screen lock and the numbers entered during a phone call (e.g., credit card and PIN numbers). Statistical results are presented to show the feasibility of such inferences and attacks.
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paper_title: Two Novel Defenses against Motion-Based Keystroke Inference Attacks

paper_content:

Nowadays smartphones come embedded with multiple motion sensors, such as an accelerometer, a gyroscope and an orientation sensor. With these sensors, apps can gather more information and therefore provide end users with more functionality. However, these sensors also introduce the potential risk of leaking a user's private information because apps can access these sensors without requiring security permissions. By monitoring a device's motion, a malicious app may be able to infer sensitive information about the owner of the device. For example, related work has shown that sensitive information entered by a user on a device's touchscreen, such as numerical PINs or passwords, can be inferred from accelerometer and gyroscope data. ::: In this paper, we study these motion-based keystroke inference attacks to determine what information they need to succeed. Based on this study, we propose two novel approaches to defend against keystroke inference attacks: 1) Reducing sensor data accuracy; 2) Random keyboard layout generation. We present the design and the implementation of these two defences on the Android platform and show how they significantly reduce the accuracy of keystroke inference attacks. We also conduct multiple user studies to evaluate the usability and feasibility of these two defences. Finally, we determine the impact of the defences on apps that have legitimate reasons to access motion sensors and show that the impact is negligible.
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paper_title: ACCessory: password inference using accelerometers on smartphones

paper_content:

We show that accelerometer readings are a powerful side channel that can be used to extract entire sequences of entered text on a smart-phone touchscreen keyboard. This possibility is a concern for two main reasons. First, unauthorized access to one's keystrokes is a serious invasion of privacy as consumers increasingly use smartphones for sensitive transactions. Second, unlike many other sensors found on smartphones, the accelerometer does not require special privileges to access on current smartphone OSes. We show that accelerometer measurements can be used to extract 6-character passwords in as few as 4.5 trials (median).
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paper_title: The Validity of a New Consumer-Targeted Wrist Device in Sleep Measurement: An Overnight Comparison Against Polysomnography in Children and Adolescents

paper_content:

STUDY OBJECTIVES ::: The validity of consumer-targeted wrist-worn sleep measurement systems has been little studied in children and adolescents. We examined the validity of a new fitness tracker (PFT) manufactured by Polar Electro Oy and the previously validated Actiwatch 2 (AW2) from Philips Respironics against polysomnography (PSG) in children and adolescents. ::: ::: ::: METHODS ::: Seventeen children (age 11.0 ± 0.8 years) and 17 adolescents (age 17.8 ± 1.8 years) wore the PFT and AW2 concurrently with an ambulatory PSG in their own home for 1 night. We compared sleep onset, offset, sleep interval (time from sleep on to offset), actual sleep time (time scored as sleep between sleep on and offset), and wake after sleep onset (WASO) between accelerometers and PSG. Sensitivity, specificity, and accuracy were calculated from the epoch-by-epoch data. ::: ::: ::: RESULTS ::: Both devices performed adequately against PSG, with excellent sensitivity for both age groups (> 0.91). In terms of specificity, the PFT was adequate in both groups (> 0.77), and AW2 adequate in children (0.68) and poor in adolescents (0.58). In the younger group, the PFT underestimated actual sleep time by 29.9 minutes and AW2 underestimated actual sleep time by 43.6 minutes. Both overestimated WASO, PFT by 24.4 minutes and AW2 by 20.9 minutes. In the older group, both devices underestimated actual sleep time (PFT by 20.6 minutes and AW2 by 26.8 minutes) and overestimated WASO (PFT by 12.5 minutes and AW2 by 14.3 minutes). Both devices were accurate in defining sleep onset. ::: ::: ::: CONCLUSIONS ::: This study suggests that this consumer-targeted wrist-worn device performs as well as, or even better than, the previously validated AW2 against PSG in children and adolescents. Both devices underestimated sleep but to a lesser extent than seen in many previous validation studies on research-targeted accelerometers.
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paper_title: Estimating Carrier’s Height by Accelerometer Signals of a Smartphone

paper_content:

The aim of this study is to estimate height of a carrier (owner) of a smartphone by using single three-axis accelerometer signals. We found that the accelerometer signals collected while a carrier goes up the stairs contain features that are correlated with height of the carrier as high as \(r = 0.801\). Apart from potentially useful applications of the result, there might arise a privacy issue in using smartphone since approximate height could be acquired in this way. Although, in general, height is not regarded as private information to be worried about, if someone thinks his/her height belongs to privacy, it should be protected against leakage.
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paper_title: Supervised learning in voice type discrimination using neck-skin vibration signals: Preliminary results on single vowels

paper_content:

Discrimination between normal and pathological voice is a critical component in laryngeal pathology diagnosis and vocal rehabilitative treatment. In the present study, a portable miniature glottal notch accelerometer (GNA) device with supervised machine learning techniques was proposed to discriminate between three human voice types: normal, breathy, and pressed voice. Fourteen native American English speakers who were wearing a GNA device produced five different English single vowels in each of the three voice types. Acoustic features of the GNA signals were extracted using spectral analysis. Preliminary assessments of feature discrepancy among different voice types were made to present physical clues of discrimination. The linear discriminant analysis technique was applied to reduce the dimensionality of the raw-feature vector of the GNA signals. Maximization of between-class distance and minimization of within-class distance were synchronously achieved. The voice types were then classified using severa...
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paper_title: A Novel, Open Access Method to Assess Sleep Duration Using a Wrist-Worn Accelerometer

paper_content:

Wrist-worn accelerometers are increasingly being used for the assessment of physical activity in population studies, but little is known about their value for sleep assessment. We developed a novel method of assessing sleep duration using data from 4,094 Whitehall II Study (United Kingdom, 2012-2013) participants aged 60-83 who wore the accelerometer for 9 consecutive days, filled in a sleep log and reported sleep duration via questionnaire. Our sleep detection algorithm defined (nocturnal) sleep as a period of sustained inactivity, itself detected as the absence of change in arm angle greater than 5 degrees for 5 minutes or more, during a period recorded as sleep by the participant in their sleep log. The resulting estimate of sleep duration had a moderate (but similar to previous findings) agreement with questionnaire based measures for time in bed, defined as the difference between sleep onset and waking time (kappa = 0.32, 95%CI:0.29,0.34) and total sleep duration (kappa = 0.39, 0.36,0.42). This estimate was lower for time in bed for women, depressed participants, those reporting more insomnia symptoms, and on weekend days. No such group differences were found for total sleep duration. Our algorithm was validated against data from a polysomnography study on 28 persons which found a longer time window and lower angle threshold to have better sensitivity to wakefulness, while the reverse was true for sensitivity to sleep. The novelty of our method is the use of a generic algorithm that will allow comparison between studies rather than a "count" based, device specific method.
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paper_title: A physiological sound sensing system using accelerometer based on flip-chip piezoelectric technology and asymmetrically gapped cantilever

paper_content:

This paper focuses on the sensing of physiological sound on human body using accelerometers. The physiological sound sensing have demanding requirements on the sensitivity/noise performance of accelerometers since the physiological sounds are usually very weak. In this paper, a piezoelectric accelerometer based on the asymmetrically gapped cantilever structure, which exhibits significantly improved sensitivity, is presented. Furthermore, in order to reduce the package size of the accelerometer, a flip-chip piezoelectric technology is proposed. The accelerometer has a resonant frequency of 1580 Hz which is much higher than the heart sound frequency range, and a quality factor of 9.2. Using a coherent scaling method, the scaled noise level of the accelerometer between 10 Hz and 400 Hz is 1 µV/√Hz. Preliminarily test results show that the signal-to-noise ratio of the heart sound signal measured by the designed accelerometer is about four times higher than that by a high-end stethoscope.
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paper_title: The Impact of Daily Sleep Duration on Health: A Review of the Literature

paper_content:

A healthy amount of sleep is paramount to leading a healthy and productive lifestyle. Although chronic sleep loss is common in today's society, many people are unaware of the potential adverse health effects of habitual sleep restriction. Under strict experimental conditions, short-term restriction of sleep results in a variety of adverse physiologic effects, including hypertension, activation of the sympathetic nervous system, impairment of glucose control, and increased inflammation. A variety of epidemiologic studies have also suggested an association between self-reported sleep duration and long-term health. Individuals who report both an increased (>8 h/d) or reduced (<7 h/d) sleep duration are at modestly increased risk of all-cause mortality, cardiovascular disease, and developing symptomatic diabetes. Although the data are not definitive, these studies suggest that sleep should not be considered a luxury, but an important component of a healthful lifestyle.
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paper_title: Cross-sectional Relationship of Pedometer-Determined Ambulatory Activity to Indicators of Health

paper_content:

Objective: To describe the cross-sectional relationship between an objective measure of walking (pedometer-determined steps/day) and general indicators of health, a prior diagnosis of one or more components of the metabolic syndrome, and self-reported occupational activity in a generally sedentary working population. ::: ::: ::: ::: Research Methods and Procedures: Steps/day were compared with previous diagnosis of one or more components of the metabolic syndrome (by self-administered questionnaire) and with general health indicators including BMI, waist circumference, resting heart rate, and blood pressure in 182 subjects in Prince Edward Island, Canada. Study participants were volunteer employees recruited from five workplaces where, in general, the job types were moderately or highly sedentary. ::: ::: ::: ::: Results: Steps/day were 7230 ± SD 3447 for women (n = 153) and 8265 ± 2849 (n = 21) for men. Pedometer-determined steps/day were associated inversely with BMI (r = −0.4005, p < 0.0001) in all participants and waist circumference in females only (r = −0.4303, p < 0.0001). There was a low correlation between steps/day and diastolic blood pressure in the whole sample (r = −0.2140, p = 0.0383). Participants who reported a prior diagnosis of one or more components of the metabolic syndrome (hypertension, hypercholesterolemia, heart disease, or type 2 diabetes) took fewer steps/day than healthy participants (p = 0.0254). Pedometer-determined steps/day were positively associated with self-reported occupational activity (p = 0.0002). ::: ::: ::: ::: Discussion: Fewer steps/day are associated with increased BMI, waist circumference, diastolic blood pressure, and components of the metabolic syndrome. Low occupational activity is a contributing factor to low total ambulatory activity.
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paper_title: Identifying user traits by mining smart phone accelerometer data

paper_content:

Smart phones are quite sophisticated and increasingly incorporate diverse and powerful sensors. One such sensor is the tri-axial accelerometer, which measures acceleration in all three spatial dimensions. The accelerometer was initially included for screen rotation and advanced game play, but can support other applications. In prior work we showed how the accelerometer could be used to identify and/or authenticate a smart phone user [11]. In this paper we extend that prior work to identify user traits such as sex, height, and weight, by building predictive models from labeled accelerometer data using supervised learning methods. The identification of such traits is often referred to as "soft biometrics" because these traits are not sufficiently distinctive or invariant to uniquely identify an individual---but they can be used in conjunction with other information for identification purposes. While our work can be used for biometric identification, our primary goal is to learn as much as possible about the smart phone user. This mined knowledge can be then be used for a number of purposes, such as marketing or making an application more intelligent (e.g., a fitness app could consider a user's weight when calculating calories burned).
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paper_title: Daily Patterns of Accelerometer Activity Predict Changes in Sleep, Cognition, and Mortality in Older Men

paper_content:

Background ::: There is growing interest in the area of "wearable tech" and its relationship to health. A common element of many of these devices is a triaxial accelerometer that can yield continuous information on gross motor activity levels; how such data might predict changes in health is less clear. ::: ::: ::: Methods ::: We examined accelerometry data from 2,976 older men who were part of the Osteoporotic Fractures in Men (MrOS) study. Using a shape-naive technique, functional principal component analysis, we examined the patterns of motor activity over the course of 4-7 days and determined whether these patterns were associated with changes in polysomnographic-determined sleep and cognitive function (Trail Making Test-Part B [Trails B], Modified Mini-Mental State Examination [3MS]), as well as mortality over 6.5-8 years of follow-up. ::: ::: ::: Results ::: In comparing baseline to 6.5 years later, multivariate modeling indicated that low daytime activity at baseline was associated with worsening of sleep efficiency (p < .05), more wake after sleep onset (p < .05), and a decrease in cognition (Trails B; p < .001), as well as a 1.6-fold higher rate of all-cause mortality (hazard ratio = 1.64 [1.34-2.00]). Earlier wake and bed times were associated with a decrease in cognition (3MS; p < .05). Having a late afternoon peak in activity was associated with a 1.4-fold higher rate of all-cause mortality (hazard ratio = 1.46 [1.21-1.77]). Those having a longer duration of their daytime activity with a bimodal activity pattern also had over a 1.4-fold higher rate of cardiovascular-related mortality (hazard ratio = 1.42 [1.02-1.98]). ::: ::: ::: Conclusions ::: Patterns of daily activity may be useful as predictive biomarkers for changes in clinically relevant outcomes, including mortality and changes in sleep and cognition in older men.
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paper_title: Estimating sleep efficiency in 10‐ to‐ 13‐year‐olds using a waist‐worn accelerometer

paper_content:

Abstract Objective In field settings, wrist- and waist-worn accelerometers are typically used to assess sleep characteristics and movement behaviors, respectively. There has been a shift in movement behavior studies to wear accelerometers 24 h/d. Sleep characteristics could be assessed in these studies if sleep algorithms were available for waist-worn accelerometers. The objective of this study was to develop and provide validity data for an algorithm/sleep likelihood score cut-off to estimate sleep efficiency in children using the waist-worn Actical accelerometer. Design Cross-sectional study. Participants Fifty healthy children aged 10-13 years. Measurements Children wore an Actical on their waist and an Actiwatch 2 on their nondominant wrist for 8 nights at home in their normal sleep environment. Participants were randomized into algorithm/sleep likelihood score "development" and "test" groups (n=25 per group). Within the development group, we assessed sleep efficiency with the Actical using the same algorithm that the Actiwatch 2 uses and selected the sleep likelihood score cut-off value that was the most accurate at predicting sleep efficiency at the nightly level compared with the Actiwatch 2. We applied this algorithm and cut-off value to the test group. Results Mean (SD) sleep efficiency estimates for the test group from the Actical and Actiwatch 2 were 89.0% (3.9%) and 88.7% (3.1%), respectively. Bland-Altman plots and absolute difference scores revealed considerable agreement between devices for both nightly and weekly estimates of sleep efficiency. Conclusion A waist-worn Actical accelerometer can accurately predict sleep efficiency in field settings among healthy 10- to 13-year-olds.
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paper_title: Investigating gender recognition in smartphones using accelerometer and gyroscope sensor readings

paper_content:

This paper presents an approach for gender recognition using behavioral biometrics in smartphones. Specifically, this work investigates gender recognition using gait data acquired from the inbuilt accelerometer and gyroscope sensors of a smartphone. The proposed approach involves computation of curvature of the gait signals. In order to capture the local variations of estimated curvatures, we employed histogram features of multi-level local pattern (MLP) and local binary pattern (LBP). In this work, support vector machine (SVM) and aggregate bootstrapping (bagging) classifiers are employed for identification of gender based on the extracted features. Performance evaluation of the proposed approach on a database of 252 gait data collected from 42 subjects yielded promising results. Our experimental results also show that MLP performs better than LBP for feature extraction, while bagging outperforms SVM for classification.
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paper_title: Adolescent patterns of physical activity differences by gender, day, and time of day.

paper_content:

BACKGROUND ::: More information about the physical activity of adolescents is needed. This study used objective measurement to investigate differences in activity patterns related to gender, body mass index (BMI), day, and time of day. ::: ::: ::: METHODS ::: Eighth-grade adolescents (37 boys, 44 girls) wore the Manufacturing Technologies Inc. (MTI) accelerometer for 4 days and kept a previous-day physical activity recall diary in the fall of 2002. Minutes per hour in sedentary, light, and moderate/vigorous activity, as recorded by the MTI, and in nine activity categories, as recorded by the diary, were calculated for three time periods (6:00 am to 2:59 pm, 3:00 pm to 6:59 pm, 7:00 pm to midnight) on each day (Thursday through Sunday). ::: ::: ::: RESULTS ::: Doubly multivariate analysis of variance revealed significant gender by day by time differences in sedentary (p =0.005) and moderate/vigorous (p <0.001) activity, but no significant BMI interactions. Except on Sunday, boys were less sedentary and more active than girls during the late afternoon period. Significant gender by category (p <0.001) and day by category (p <0.001) interactions were also found in the log data. Boys spent more time engaged in TV/electronics and sports, while girls spent more time in personal care. Three activity categories (sports, social interaction, active transportation) stayed at consistent levels across days, while others varied widely by day of the week. ::: ::: ::: CONCLUSIONS ::: Except on Sunday, consistent gender differences were found in activity levels, especially for the late afternoon period. Significant increases in sitting, TV/electronic games, and chores were seen for weekend days. Results support strategies to reduce sitting and electronic recreation, which may increase physical activity.
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paper_title: Identifying user traits by mining smart phone accelerometer data

paper_content:

Smart phones are quite sophisticated and increasingly incorporate diverse and powerful sensors. One such sensor is the tri-axial accelerometer, which measures acceleration in all three spatial dimensions. The accelerometer was initially included for screen rotation and advanced game play, but can support other applications. In prior work we showed how the accelerometer could be used to identify and/or authenticate a smart phone user [11]. In this paper we extend that prior work to identify user traits such as sex, height, and weight, by building predictive models from labeled accelerometer data using supervised learning methods. The identification of such traits is often referred to as "soft biometrics" because these traits are not sufficiently distinctive or invariant to uniquely identify an individual---but they can be used in conjunction with other information for identification purposes. While our work can be used for biometric identification, our primary goal is to learn as much as possible about the smart phone user. This mined knowledge can be then be used for a number of purposes, such as marketing or making an application more intelligent (e.g., a fitness app could consider a user's weight when calculating calories burned).
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paper_title: Female gait patterns in shoes with different heel heights

paper_content:

The gait patterns of twelve women were compared during walking in flat shoes and in shoes with high heels. In addition, four anthropometric measurements were recorded. Results showed that the wearing of high heels caused a significant decrease in step length and stride length, whereas there was a minimum change in the stride width and foot angle
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paper_title: Age group detection using smartphone motion sensors

paper_content:

Side-channel attacks revealing the sensitive user data through the motion sensors (such as accelerometer, gyroscope, and orientation sensors) emerged as a new trend in the smartphone security. In this respect, recent studies have examined feasibility of inferring user's tap input by utilizing the motion sensor readings and propounded that some user secrets can be deduced by adopting the different side-channel attacks. More precisely, in this kind of attacks, a malware processes outputs of these sensors to exfiltrate victims private information such as PINs, passwords or unlock patterns. In this paper, we describe a new side-channel attack on smartphones that aims to predict the age interval of the user. Unlike the previous works, our attack does not directly deal with recovering a target user's some secret, rather its sole purpose is determining whether she is a child or an adult. The main idea behind our study relies on the key observation that the characteristics of children and adults differ in hand holding and touching the smartphones. Consequently, we show that there is an apparent correlation between the motion sensor readings and these characteristics that build up our attack strategy. In order to exhibit efficiency of the proposed attack, we have developed an Android application named as BalloonLogger that evaluates accelerometer sensor data and perform child/adult detection with a success rate of 92.5%. To the best of our knowledge, in this work, for the first time, we point out such a security breach.
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paper_title: Automatic Stress Detection in Working Environments from Smartphones' Accelerometer Data: A First Step

paper_content:

Increase in workload across many organizations and consequent increase in occupational stress are negatively affecting the health of the workforce. Measuring stress and other human psychological dynamics is difficult due to subjective nature of selfreporting and variability between and within individuals. With the advent of smartphones, it is now possible to monitor diverse aspects of human behavior, including objectively measured behavior related to psychological state and consequently stress. We have used data from the smartphone's built-in accelerometer to detect behavior that correlates with subjects stress levels. Accelerometer sensor was chosen because it raises fewer privacy concerns (e.g., in comparison to location, video, or audio recording), and because its low-power consumption makes it suitable to be embedded in smaller wearable devices, such as fitness trackers. About 30 subjects from two different organizations were provided with smartphones. The study lasted for eight weeks and was conducted in real working environments, with no constraints whatsoever placed upon smartphone usage. The subjects reported their perceived stress levels three times during their working hours. Using combination of statistical models to classify selfreported stress levels, we achieved a maximum overall accuracy of 71% for user-specific models and an accuracy of 60% for the use of similar-users models, relying solely on data from a single accelerometer.
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paper_title: Emotion recognition based on customized smart bracelet with built-in accelerometer

paper_content:

BACKGROUND ::: Recently, emotion recognition has become a hot topic in human-computer interaction. If computers could understand human emotions, they could interact better with their users. This paper proposes a novel method to recognize human emotions (neutral, happy, and angry) using a smart bracelet with built-in accelerometer. ::: ::: ::: METHODS ::: In this study, a total of 123 participants were instructed to wear a customized smart bracelet with built-in accelerometer that can track and record their movements. Firstly, participants walked two minutes as normal, which served as walking behaviors in a neutral emotion condition. Participants then watched emotional film clips to elicit emotions (happy and angry). The time interval between watching two clips was more than four hours. After watching film clips, they walked for one minute, which served as walking behaviors in a happy or angry emotion condition. We collected raw data from the bracelet and extracted a few features from raw data. Based on these features, we built classification models for classifying three types of emotions (neutral, happy, and angry). ::: ::: ::: RESULTS AND DISCUSSION ::: For two-category classification, the classification accuracy can reach 91.3% (neutral vs. angry), 88.5% (neutral vs. happy), and 88.5% (happy vs. angry), respectively; while, for the differentiation among three types of emotions (neutral, happy, and angry), the accuracy can reach 81.2%. ::: ::: ::: CONCLUSIONS ::: Using wearable devices, we found it is possible to recognize human emotions (neutral, happy, and angry) with fair accuracy. Results of this study may be useful to improve the performance of human-computer interaction.
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paper_title: Smartphone accelerometer data used for detecting human emotions

paper_content:

The paper outlines work on the classification of emotions using smartphone accelerometer data. Such classification can be used, in conjunction with other methods of emotion detection, to adapt services to the user's emotional state. The data is collected from individuals who have been carrying their phone in a pocket while walking. An Android app was developed in order to monitor the smartphone accelerometer of the individuals who participated in the study and occasionally requested them to judge and submit their emotional state. This way, data is collected from a natural environment rather than a laboratory setting. The recorded data is then processed and used to train different classifiers to be compared. The machine learning algorithms decision tree, support vector machine and multilayer perceptron are used for this purpose. Emotions are classified in two dimensions: pleasantness and arousal (activation). While the recognition rate for the arousal dimension is promising at 75%, pleasantness is harder to predict, with a recognition rate of 51%. These findings indicate that by only analyzing accelerometer data recorded from a smartphone, it is possible to make predictions of a person's state of activation.
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paper_title: Be Active and Become Happy: An Ecological Momentary Assessment of Physical Activity and Mood

paper_content:

The positive effects of physical activity on mood are well documented in cross-sectional studies. To date there have been only a few studies analyzing within-subject covariance between physical activity and mood in everyday life. This study aims to close this gap using an ambulatory assessment of mood and physical activity. Thirteen participants completed a standardized diary over a 10-week period, resulting in 1,860 measurement points. Valence, energetic arousal, and calmness are the three subscales of mood that were assessed. Participants rated their mood promptly after self-selected activities. A multilevel analysis indicates that the three dimensions of mood were positively affected by episodes of physical activity, such as walking or gardening—valence: t(12) = 5.6, p < .001; energetic arousal: t(12) = 2.4, p = .033; calmness: t(12) = 2.8, p = .015. Moreover, the association is affected by the individual baseline mood level, with the greatest effect seen when mood is depressed.
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paper_title: Mining for motivation: using a single wearable accelerometer to detect people's interests

paper_content:

This paper presents a novel investigation of how motion as measured with just a single wearable accelerometer is informative of people's interests and motivation during crowded social events. We collected accelerometer readings on a large number of people (32 and 46 people in two crowded social events involving up to hundreds of people). In our experiments, we demonstrate how people's movements are informative of their particular interests: during talks, their interests in particular topics, and during networking events, their interest to participate successfully to make new contacts and foster existing ones. To our knowledge, using a single body worn accelerometer to measure and automatically infer these aspects of social behaviour has never been attempted before. Our experiments show that despite the challenge of the proposed task, useful automated predictions are possible and demonstrate the potential for further research in this area.
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paper_title: Personality and physical activity: A systematic review and meta-analysis.

paper_content:

Abstract Whether personality determines physical activity or its outcomes is relevant for theory and public health but has been understudied. We estimated the population correlations between Big-Five personality factors and physical activity and examined whether they varied according to sample characteristics and study features. Database searches were conducted according to PRISMA guidelines, for articles published in the English language prior to November 1st, 2013. Sixty-four studies including a total of 88,400 participants yielded effects (k) for Extraversion (88), Neuroticism (82), Conscientiousness (69), Openness (51) and Agreeableness (52). Significant mean r was found for Extraversion (r = .1076), Neuroticism (r = −.0710), Conscientiousness (r = .1037) and Openness (r = .0344), but not Agreeableness (r = .0020). Effects were moderately heterogeneous (I2 range = 44–65%) and varied by sample characteristics (e.g., age, gender, or clinical status) and/or study features (e.g., measure quality or item format). This analysis expands results of previous reviews and provides new support for a relationship between physical activity and Openness. Future studies should use better measures of physical activity and prospective designs, adjust for statistical artifacts, and consider advances in the conceptualization of personality.
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paper_title: Personality and Actigraphy-Measured Physical Activity in Older Adults

paper_content:

Most studies on personality and physical activity have relied on self-report measures. This study examined the relation between Five Factor Model personality traits and objective physical activity in older adults. Sixty-nine participants (Mage = 80.2 years; SD = 7.1) wore the ActiGraph monitor for 7 days and completed the NEO Personality Inventory-3 First Half. Extraversion, Agreeableness, and Conscientiousness were associated with more moderate physical activity and more steps per day whereas Neuroticism was inversely related to these physical activity measures (βs > .20). The associations for Neuroticism and Conscientiousness were attenuated by approximately 20-40% when accounting for disease burden and body mass index but were essentially unchanged for Extraversion and Agreeableness. These findings confirm self-report evidence that personality traits are associated with physical activity levels in older adults. (PsycINFO Database Record
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paper_title: Detection of Activities by Wireless Sensors for Daily Life Surveillance: Eating and Drinking

paper_content:

This paper introduces a two-stage approach to the detection of people eating and/or drinking for the purposes of surveillance of daily life. With the sole use of wearable accelerometer sensor attached to somebody’s (man or a woman) wrists, this two-stage approach consists of feature extraction followed by classification. At the first stage, based on the limb’s three dimensional kinematics movement model and the Extended Kalman Filter (EKF), the realtime arm movement features described by Euler angles are extracted from the raw accelerometer measurement data. In the latter stage, the Hierarchical Temporal Memory (HTM) network is adopted to classify the extracted features of the eating/drinking activities based on the space and time varying property of the features, by making use of the powerful modelling capability of HTM network on dynamic signals which is varying with both space and time. The proposed approach is tested through the real eating and drinking activities using the three dimensional accelerometers. Experimental results show that the EKF and HTM based two-stage approach can perform the activity detection successfully with very high accuracy.
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paper_title: Driving Behavior and Traffic Safety: An Acceleration-Based Safety Evaluation Procedure for Smartphones

paper_content:

Traffic safety and energy efficiency of vehicles are strictly related to driver’s behavior. The scientific literature has investigated on some specific dynamic parameters that, among the others, can be used as a measure of unsafe or aggressive driving style such as longitudinal and lateral acceleration of vehicle. Moreover, the use of modern mobile devices (smartphones and tablets), and their internal sensors (GPS receivers, three-axes accelerometers), allows road users to receive real time information and feedback that can be useful to increase awareness of drivers and promote safety. This paper focuses on the development of a prototype mobile application that can evaluate the grade of safety that drivers are keeping on the road by measuring of accelerations (longitudinal and lateral) and warning for users when it can be convenient to correct their driving style. The aggressiveness is evaluated by plotting vehicle’s acceleration on a g-g diagram specially studied and designed, where horizontal and lateral acceleration is displayed inside areas of “Good Driving Style”. Several experimental tests were carried out with different drivers and cars in order to estimate the system accuracy and the usability of the application. This work is part of the wider research project M2M, Mobile to Mobility: Information and communication technology systems for road traffic safety (PON National Operational Program for Research and Competitiveness 2007-2013) which is based on the use of mobile sensor computing systems for giving real-time information in order to reduce risks and to make the transportation system more safe and comfortable.
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paper_title: Estimating Carrier’s Height by Accelerometer Signals of a Smartphone

paper_content:

The aim of this study is to estimate height of a carrier (owner) of a smartphone by using single three-axis accelerometer signals. We found that the accelerometer signals collected while a carrier goes up the stairs contain features that are correlated with height of the carrier as high as \(r = 0.801\). Apart from potentially useful applications of the result, there might arise a privacy issue in using smartphone since approximate height could be acquired in this way. Although, in general, height is not regarded as private information to be worried about, if someone thinks his/her height belongs to privacy, it should be protected against leakage.
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paper_title: Feature Selection and Activity Recognition System Using a Single Triaxial Accelerometer

paper_content:

Activity recognition is required in various applications such as medical monitoring and rehabilitation. Previously developed activity recognition systems utilizing triaxial accelerometers have provided mixed results, with subject-to-subject variability. This paper presents an accurate activity recognition system utilizing a body worn wireless accelerometer, to be used in the real-life application of patient monitoring. The algorithm utilizes data from a single, waist-mounted triaxial accelerometer to classify gait events into six daily living activities and transitional events. The accelerometer can be worn at any location around the circumference of the waist, thereby reducing user training. Feature selection is performed using Relief-F and sequential forward floating search (SFFS) from a range of previously published features, as well as new features introduced in this paper. Relevant and robust features that are insensitive to the positioning of accelerometer around the waist are selected. SFFS selected almost half the number of features in comparison to Relief-F and provided higher accuracy than Relief-F. Activity classification is performed using Naive Bayes and $k$ -nearest neighbor $(k$ -NN) and the results are compared. Activity recognition results on seven subjects with leave-one-person-out error estimates show an overall accuracy of about 98% for both the classifiers. Accuracy for each of the individual activity is also more than 95%.
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paper_title: Mobile phone based drunk driving detection

paper_content:

Drunk driving, or officially Driving Under the Influence (DUI) of alcohol, is a major cause of traffic accidents throughout the world. In this paper, we propose a highly efficient system aimed at early detection and alert of dangerous vehicle maneuvers typically related to drunk driving. The entire solution requires only a mobile phone placed in vehicle and with accelerometer and orientation sensor. A program installed on the mobile phone computes accelerations based on sensor readings, and compares them with typical drunk driving patterns extracted from real driving tests. Once any evidence of drunk driving is present, the mobile phone will automatically alert the driver or call the police for help well before accident actually happens. We implement the detection system on Android G1 phone and have it tested with different kinds of driving behaviors. The results show that the system achieves high accuracy and energy efficiency.
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paper_title: Detection of posture and motion by accelerometry: a validation study in ambulatory monitoring

paper_content:

The suitable placement of a small number of calibrated piezoresistive accelerometer devices may suffice to assess postures and motions reliably. This finding, which was obtained in a previous investigation, led to the further development of this methodology and to an extension from the laboratory to conditions of daily life. The intention was to validate the accelerometric assessment against behavior observation and to examine the retest reliability. Twenty-four participants were recorded, according to a standard protocol consisting of nine postures/motions (repeated once) which served as reference patterns. The recordings were continued outside the laboratory. A participant observer classified the postures and motions. Four sensor placements (sternum, wrist, thigh, and lower leg) were used. The findings indicated that the detection of posture and motion based on accelerometry is highly reliable. The correlation between behavior observation and kinematic analysis was satisfactory, although some participants showed discrepancies regarding specific motions.
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paper_title: Whose move is it anyway? Authenticating smart wearable devices using unique head movement patterns

paper_content:

In this paper, we present the design, implementation and evaluation of a user authentication system, Headbanger, for smart head-worn devices, through monitoring the user's unique head-movement patterns in response to an external audio stimulus. Compared to today's solutions, which primarily rely on indirect authentication mechanisms via the user's smartphone, thus cumbersome and susceptible to adversary intrusions, the proposed head-movement based authentication provides an accurate, robust, light-weight and convenient solution. Through extensive experimental evaluation with 95 participants, we show that our mechanism can accurately authenticate users with an average true acceptance rate of 95.57% while keeping the average false acceptance rate of 4.43%. We also show that even simple head-movement patterns are robust against imitation attacks. Finally, we demonstrate our authentication algorithm is rather light-weight: the overall processing latency on Google Glass is around 1.9 seconds.
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paper_title: We Can Track You if You Take the Metro: Tracking Metro Riders Using Accelerometers on Smartphones

paper_content:

Motion sensors, especially accelerometers, on smartphones have been discovered to be a powerful side channel for spying on users’ privacy. In this paper, we reveal a new accelerometer-based side-channel attack which is particularly serious: malware on smartphones can easily exploit the accelerometers to trace metro riders stealthily. We first address the challenge to automatically filter out metro-related data from a mass of miscellaneous accelerometer readings, and then propose a basic attack which leverages an ensemble interval classifier built from supervised learning to infer the riding trajectory of the user. As the supervised learning requires the attacker to collect labeled training data for each station interval, this attack confronts the scalability problem in big cities with a huge metro network. We thus further present an improved attack using semi-supervised learning, which only requires the attacker to collect labeled data for a very small number of distinctive station intervals. We conduct real experiments on a large self-built dataset, which contains more than 120 h of data collected from six metro lines of three major cities. The results show that the inferring accuracy could reach 89% and 94% if the user takes the metro for four and six stations, respectively. We finally discuss possible countermeasures against the proposed attack.
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paper_title: Accelerometer's position free human activity recognition using a hierarchical recognition model

paper_content:

Monitoring of physical activities is a growing field with potential applications such as lifecare and healthcare. Accelerometry shows promise in providing an inexpensive but effective means of long-term activity monitoring of elderly patients. However, even for the same physical activity the output of any body-worn Triaxial Accelerometer (TA) varies at different positions of a subject's body, resulting in a high within-class variance. Thus almost all existing TA-based human activity recognition systems require firm attachment of TA to a specific body part, making them impractical for long-term activity monitoring during unsupervised free living. Therefore, we present a novel hierarchical recognition model that can recognize human activities independent of TA's position along a human body. The proposed model minimizes the high within-class variance significantly and allows subjects to carry TA freely in any pocket without attaching it firmly to a body-part. We validated our model using six daily physical activities: resting (sit/stand), walking, walk-upstairs, walk-downstairs, running, and cycling. Activity data is collected from four most probable body positions of TA: chest pocket, front trousers pocket, rear trousers pocket, and inner jacket pocket. The average accuracy of about 95% illustrates the effectiveness of the proposed method.
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paper_title: Authentication of Smartphone Users Based on the Way They Walk Using k-NN Algorithm

paper_content:

Accelerometer-based biometric gait recognition offers a convenient way to authenticate users on their mobile devices. Modern smartphones contain in-built accelerometers which can be used as sensors to acquire the necessary data while the subjects are walking. Hence, no additional costs for special sensors are imposed to the user. In this publication we extract several features from the gait data and use the k-Nearest Neighbour algorithm for classification. We show that this algorithm yields a better biometric performance than the machine learning algorithms we previously used for classification, namely Hidden Markov Models and Support Vector Machines. We implemented the presented method on a smartphone and demonstrate that it is efficient enough to be applied in practice.
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paper_title: Context-Aware Active Authentication Using Smartphone Accelerometer Measurements

paper_content:

While body movement patterns recorded by a smartphone accelerometer are now well understood to be discriminative enough to separate users, little work has been done to address the question of if or how the position in which the phone is held affects user authentication. In this work, we show through a combination of supervised learning methods and statistical tests, that there are certain users for whom exploitation of information of how a phone is held drastically improves classification performance. We propose a two-stage authentication framework that identifies the location of the phone before performing authentication, and show its benefits based on a dataset of 30 users. Our work represents a first step towards bridging the gap between accelerometer-based authentication systems analyzed from the context of a laboratory environment and a real accelerometer-based authentication system in the wild where phone positioning cannot be assumed.
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paper_title: Estimating sleep efficiency in 10‐ to‐ 13‐year‐olds using a waist‐worn accelerometer

paper_content:

Abstract Objective In field settings, wrist- and waist-worn accelerometers are typically used to assess sleep characteristics and movement behaviors, respectively. There has been a shift in movement behavior studies to wear accelerometers 24 h/d. Sleep characteristics could be assessed in these studies if sleep algorithms were available for waist-worn accelerometers. The objective of this study was to develop and provide validity data for an algorithm/sleep likelihood score cut-off to estimate sleep efficiency in children using the waist-worn Actical accelerometer. Design Cross-sectional study. Participants Fifty healthy children aged 10-13 years. Measurements Children wore an Actical on their waist and an Actiwatch 2 on their nondominant wrist for 8 nights at home in their normal sleep environment. Participants were randomized into algorithm/sleep likelihood score "development" and "test" groups (n=25 per group). Within the development group, we assessed sleep efficiency with the Actical using the same algorithm that the Actiwatch 2 uses and selected the sleep likelihood score cut-off value that was the most accurate at predicting sleep efficiency at the nightly level compared with the Actiwatch 2. We applied this algorithm and cut-off value to the test group. Results Mean (SD) sleep efficiency estimates for the test group from the Actical and Actiwatch 2 were 89.0% (3.9%) and 88.7% (3.1%), respectively. Bland-Altman plots and absolute difference scores revealed considerable agreement between devices for both nightly and weekly estimates of sleep efficiency. Conclusion A waist-worn Actical accelerometer can accurately predict sleep efficiency in field settings among healthy 10- to 13-year-olds.
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paper_title: Activity Recognition Using a Single Accelerometer Placed at the Wrist or Ankle

paper_content:

AB Purpose: Large physical activity surveillance projects such as the UK Biobank and NHANES are using wrist-worn accelerometer-based activity monitors that collect raw data. The goal is to increase wear time by asking subjects to wear the monitors on the wrist instead of the hip, and then to use information in the raw signal to improve activity type and intensity estimation. The purposes of this work was to obtain an algorithm to process wrist and ankle raw data and to classify behavior into four broad activity classes: ambulation, cycling, sedentary, and other activities. Methods: Participants (N = 33) wearing accelerometers on the wrist and ankle performed 26 daily activities. The accelerometer data were collected, cleaned, and preprocessed to extract features that characterize 2-, 4-, and 12.8-s data windows. Feature vectors encoding information about frequency and intensity of motion extracted from analysis of the raw signal were used with a support vector machine classifier to identify a subject's activity. Results were compared with categories classified by a human observer. Algorithms were validated using a leave-one-subject-out strategy. The computational complexity of each processing step was also evaluated. Results: With 12.8-s windows, the proposed strategy showed high classification accuracies for ankle data (95.0%) that decreased to 84.7% for wrist data. Shorter (4 s) windows only minimally decreased performances of the algorithm on the wrist to 84.2%. Conclusions: A classification algorithm using 13 features shows good classification into the four classes given the complexity of the activities in the original data set. The algorithm is computationally efficient and could be implemented in real time on mobile devices with only 4-s latency.
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paper_title: Cell phone-based biometric identification

paper_content:

Mobile devices are becoming increasingly sophisticated and now incorporate many diverse and powerful sensors. The latest generation of smart phones is especially laden with sensors, including GPS sensors, vision sensors (cameras), audio sensors (microphones), light sensors, temperature sensors, direction sensors (compasses), and acceleration sensors. In this paper we describe and evaluate a system that uses phone-based acceleration sensors, called accelerometers, to identify and authenticate cell phone users. This form of behavioral biometrie identification is possible because a person's movements form a unique signature and this is reflected in the accelerometer data that they generate. To implement our system we collected accelerometer data from thirty-six users as they performed normal daily activities such as walking, jogging, and climbing stairs, aggregated this time series data into examples, and then applied standard classification algorithms to the resulting data to generate predictive models. These models either predict the identity of the individual from the set of thirty-six users, a task we call user identification, or predict whether (or not) the user is a specific user, a task we call user authentication. This work is notable because it enables identification and authentication to occur unobtrusively, without the users taking any extra actions-all they need to do is carry their cell phones. There are many uses for this work. For example, in environments where sharing may take place, our work can be used to automatically customize a mobile device to a user. It can also be used to provide device security by enabling usage for only specific users and can provide an extra level of identity verification.
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paper_title: ACCessory: password inference using accelerometers on smartphones

paper_content:

We show that accelerometer readings are a powerful side channel that can be used to extract entire sequences of entered text on a smart-phone touchscreen keyboard. This possibility is a concern for two main reasons. First, unauthorized access to one's keystrokes is a serious invasion of privacy as consumers increasingly use smartphones for sensitive transactions. Second, unlike many other sensors found on smartphones, the accelerometer does not require special privileges to access on current smartphone OSes. We show that accelerometer measurements can be used to extract 6-character passwords in as few as 4.5 trials (median).
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paper_title: puffMarker: a multi-sensor approach for pinpointing the timing of first lapse in smoking cessation

paper_content:

Recent researches have demonstrated the feasibility of detecting smoking from wearable sensors, but their performance on real-life smoking lapse detection is unknown. In this paper, we propose a new model and evaluate its performance on 61 newly abstinent smokers for detecting a first lapse. We use two wearable sensors --- breathing pattern from respiration and arm movements from 6-axis inertial sensors worn on wrists. In 10-fold cross-validation on 40 hours of training data from 6 daily smokers, our model achieves a recall rate of 96.9%, for a false positive rate of 1.1%. When our model is applied to 3 days of post-quit data from 32 lapsers, it correctly pinpoints the timing of first lapse in 28 participants. Only 2 false episodes are detected on 20 abstinent days of these participants. When tested on 84 abstinent days from 28 abstainers, the false episode per day is limited to 1/6.
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paper_title: A Triaxial Accelerometer-Based Physical-Activity Recognition via Augmented-Signal Features and a Hierarchical Recognizer

paper_content:

Physical-activity recognition via wearable sensors can provide valuable information regarding an individual's degree of functional ability and lifestyle. In this paper, we present an accelerometer sensor-based approach for human-activity recognition. Our proposed recognition method uses a hierarchical scheme. At the lower level, the state to which an activity belongs, i.e., static, transition, or dynamic, is recognized by means of statistical signal features and artificial-neural nets (ANNs). The upper level recognition uses the autoregressive (AR) modeling of the acceleration signals, thus, incorporating the derived AR-coefficients along with the signal-magnitude area and tilt angle to form an augmented-feature vector. The resulting feature vector is further processed by the linear-discriminant analysis and ANNs to recognize a particular human activity. Our proposed activity-recognition method recognizes three states and 15 activities with an average accuracy of 97.9% using only a single triaxial accelerometer attached to the subject's chest.
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Title: Privacy implications of accelerometer data: a review of possible inferences

Section 1: INTRODUCTION
Description 1: Provide an overview of accelerometers, their common applications in mobile devices, and the potential privacy concerns associated with them.

Section 2: POSSIBLE INFERENCES
Description 2: Introduce and summarize the different types of sensitive information that can be inferred from accelerometer data, categorized into sub-sections.

Section 2.1: Activity and Behavior Tracking
Description 2.1: Discuss how accelerometer data can be used to track various activities and behaviors of a user, including physical activities, sleep patterns, gestures, and more.

Section 2.2: Location Tracking
Description 2.2: Explain how accelerometer data can be utilized for tracking a user's location and deducing travel trajectories, even without GPS.

Section 2.3: User Identification
Description 2.3: Describe methods of identifying users based on their movement patterns and other unique biometric features captured through accelerometers.

Section 2.4: Keystroke Logging
Description 2.4: Detail how accelerometer data can be used to infer text inputs from touchscreen and keyboard interactions, posing privacy risks.

Section 2.5: Inference of Health Parameters and Body Features
Description 2.5: Elaborate on how health and body-related information can be derived from accelerometer data, including weight, height, and various health conditions.

Section 2.6: Inference of Demographics
Description 2.6: Discuss how demographic variables such as age and gender can be estimated using accelerometer data.

Section 2.7: Mood and Emotion Recognition
Description 2.7: Explore the potential of using accelerometer data to infer a user's mood and emotional states.

Section 2.8: Inference of Personality Traits
Description 2.8: Discuss methods for deducing personality traits from body movements and physical activity patterns measured by accelerometers.

Section 3: DISCUSSION AND IMPLICATIONS
Description 3: Reflect on the findings and discuss the broader implications for user privacy, potential threats, and technical and legal protection measures.

Section 4: CONCLUSION
Description 4: Summarize the key points of the paper and highlight the importance of reconsidering the privacy implications of accelerometer data in mobile devices.