A Review of Parkinson's Disease Cardinal and Dyskinetic Motor Symptoms Assessment Methods Using Sensor Systems
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paper_title: Trunk sway measurements during stance and gait tasks in Parkinson's disease.

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To achieve a unified assessment of postural instability in Parkinson's disease (PD) over a range of clinical stance and gait tasks, which may provide an insight into a tendency to fall, we measured trunk sway in the anterior-posterior and medial-lateral directions in freely moving PD patients and age-matched controls. We also measured task duration as time to complete the task or time to loss of balance. Patients had larger amplitudes of trunk sway velocities for stance tasks (e.g. mean pitch velocity when standing on two-legs eyes closed equalled 19.1 +/- 6.4 for PD patients on medication versus 4.8 +/- 0.3 degrees/s for controls, p = 0.0003) and for an expected (following prior warning) retropulsion test (mean roll angle equalled 4.3 +/- 0.5 degrees for PD patients versus 2.2 +/- 0.6 degrees for controls, p = 0.0003) than controls. Patients were more likely to fall earlier for stance tasks, and took longer to complete gait tasks (e.g. walking 3 m eyes closed, mean time 6.8 +/- 0.6 sees versus 4.9 +/- 0.1 sees, p = 0.0001). These differences between patients and controls were, in most cases, independent of medication. Based on these results we defined a simple test battery of stance and gait tasks that could discriminate between PD patients who had recent falls and controls. These results indicate that trunk sway measures recorded during stance and gait tasks provide useful information on balance deficits leading to falls in PD patients.
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paper_title: Detecting and monitoring the symptoms of Parkinson's disease using smartphones: A pilot study

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Background: Remote, non-invasive and objective tests that can be used to support expert diagnosis for Parkinson's disease (PD) are lacking. Methods: Participants underwent baseline in-clinic assessments, including the Unified Parkinson's Disease Rating Scale (UPDRS), and were provided smartphones with an Android operating system that contained a smartphone application that assessed voice, posture, gait, finger tapping, and response time. Participants then took the smart phones home to perform the five tasks four times a day for a month. Once a week participants had a remote (telemedicine) visit with a Parkinson disease specialist in which a modified (excluding assessments of rigidity and balance) UPDRS performed. Using statistical analyses of the five tasks recorded using the smartphone from 10 individuals with PD and 10 controls, we sought to: (1) discriminate whether the participant had PD and (2) predict the modified motor portion of the UPDRS. Results: Twenty participants performed an average of 2.7 tests per day (68.9% adherence) for the study duration (average of 34.4 days) in a home and community setting. The analyses of the five tasks differed between those with Parkinson disease and those without. In discriminating participants with PD from controls, the mean sensitivity was 96.2% (SD 2%) and mean specificity was 96.9% (SD 1.9%). The mean error in predicting the modified motor component of the UPDRS (range 11-34) was 1.26 UPDRS points (SD 0.16). Conclusion: Measuring PD symptoms via a smartphone is feasible and has potential value as a diagnostic support tool.
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paper_title: Ambulatory quantitative assessment of body position, bradykinesia, and hypokinesia in Parkinson's disease.

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We used ambulatory monitoring to quantify body position, bradykinesia, and hypokinesia simultaneously in 50 patients with Parkinson's disease (PD) and 43 healthy elderly during the diurnal period. Reliable automatic detection of three defined body positions proved possible. As compared with controls, PD patients spent less time upright and more time during the day lying down, which correlated well with the self-reported time spent lying down. PD patients had significantly lower mean values of extremity acceleration and higher mean values of immobility than controls. The objective measures of bradykinesia and hypokinesia showed only a modest or no relation to the semiquantitative subjective Unified Parkinson's Disease Rating Scale (UPDRS) motor scores, which most likely was due to differences between the methods. In contrast to bradykinesia measures, hypokinesia measures showed clear sex differences in both patients and controls. Over time, trunk and arm movements occurred more frequently in women than in men. Our ambulatory monitoring assessment disclosed clinically relevant information about the mobility profile and offers a way to quantify cardinal movement features simultaneously in PD patients throughout the day.
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paper_title: Dyskinesia and motor state detection in Parkinson's Disease patients with a single movement sensor

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Parkinson's Disease (PD) is a neurodegenerative disease that alters the patients' motor performance. Patients suffer many motor symptoms: bradykinesia, dyskinesia and freezing of gait, among others. Furthermore, patients alternate between periods in which they are able to move smoothly for some hours (ON state), and periods with motor complications (OFF state). An accurate report of PD motor states and symptoms will enable doctors to personalize medication intake and, therefore, improve response to treatment. Additionally, real-time reporting could allow an automatic management of PD by means of an automatic control of drug-administration pump doses. Such a system must be able to provide accurate information without disturbing the patients' daily life activities. This paper presents the results of the MoMoPa study classifying motor states and dyskinesia from 20 PD patients by using a belt-worn single tri-axial accelerometer. The algorithms obtained will be validated in a further study with 15 PD patients and will be enhanced in the REMPARK project.
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paper_title: Feasibility of home-based automated Parkinson's disease motor assessment

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Patients with Parkinson's disease (PD) receive therapies aimed at addressing a diverse range of motor symptoms. Motor complications in the form of symptom fluctuations and dyskinesias that commonly occur with chronic PD medication use may not be effectively captured by Unified Parkinson's Disease Rating Scale (UPDRS) assessments performed in the clinic. Therefore, home monitoring may be a viable adjunct tool to provide insight into PD motor symptom response to treatment. In this pilot study, we sought to evaluate the feasibility of capturing PD motor symptoms at home using a computer-based assessment system. Ten subjects diagnosed with idiopathic PD used the system at home and ten non-PD control subjects used the system in a laboratory. The Kinesia system consists of a wireless finger-worn motion sensor and a laptop computer with software for automated tremor and bradykinesia severity score assessments. Data from control subjects were used to develop compliance algorithms for rejecting motor tasks performed incorrectly. These algorithms were then applied to data collected from the PD subjects who used the Kinesia system at home to complete motor exams 3-6 times per day over 3-6 days. Motor tasks not rejected by the compliance algorithms were further processed for symptom severity. PD subjects successfully completed motor assessments at home, with approximately 97% of all motor task data files (1222/1260) accepted. These findings suggest that objective home monitoring of PD motor fluctuations is feasible.
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paper_title: Dyskinesia detection and monitoring by a single sensor in patients with Parkinson's disease.

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OBJECTIVE ::: In current clinical practice, assessment of levodopa-induced dyskinesias (LIDs) in Parkinson's disease (PD) is based on semiquantitative scales or patients' diaries. We aimed to assess the feasibility, clinical validity, and usability of a waist-worn inertial sensor for discriminating between LIDs and physiological sway in both supervised and unsupervised settings. ::: ::: ::: METHODS ::: Forty-six PD patients on L-dopa therapy, 18 de novo PD patients, and 18 healthy controls were enrolled. Patients underwent clinical assessment of motor signs and dyskinesias and kinetic-dynamic L-dopa monitoring, tracked by serial measurements of plasma drug concentrations and motor and postural tests. ::: ::: ::: RESULTS ::: A subset of features was selected, which showed excellent reliability. Sensitivity and specificity of the selected features for dyskinesia recognition were assessed in both supervised and unsupervised settings with an accuracy of 95% and 86%, respectively. ::: ::: ::: CONCLUSIONS ::: Our preliminary findings suggest that it is feasible to design a reliable sensor-based application for dyskinesia monitoring at home.
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paper_title: Quantification of Tremor and Bradykinesia in Parkinson's Disease Using a Novel Ambulatory Monitoring System

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An ambulatory system for quantification of tremor and bradykinesia in patients with Parkinson's disease (PD) is presented. To record movements of the upper extremities, a sensing units which included miniature gyroscopes, has been fixed to each of the forearms. An algorithm to detect and quantify tremor and another algorithm to quantify bradykinesia have been proposed and validated. Two clinical studies have been performed. In the first study, 10 PD patients and 10 control subjects participated in a 45-min protocol of 17 typical daily activities. The algorithm for tremor detection showed an overall sensitivity of 99.5% and a specificity of 94.2% in comparison to a video reference. The estimated tremor amplitude showed a high correlation to the Unified Parkinson's Disease Rating Scale (UPDRS) tremor subscore (e.g., r=0.87, p<0.001 for the roll axis). There was a high and significant correlation between the estimated bradykinesia related parameters estimated for the whole period of measurement and respective UPDRS subscore (e.g., r=-0.83, p<0.001 for the roll axis). In the second study, movements of upper extremities of 11 PD patients were recorded for periods of 3-5 hr. The patients were moving freely during the measurements. The effects of selection of window size used to calculate tremor and bradykinesia related parameters on the correlation between UPDRS and these parameters were studied. By selecting a window similar to the period of the first study, similar correlations were obtained. Moreover, one of the bradykinesia related parameters showed significant correlation (r=-0.74, p<0.01) to UPDRS with window sizes as short as 5 min. Our study provides evidence that objective, accurate and simultaneous assessment of tremor and bradykinesia can be achieved in free moving PD patients during their daily activities
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paper_title: Quantifying drug-induced dyskinesias in the arms using digitised spiral-drawing tasks

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In this study, we quantify the severity of drug-induced dyskinesias in the arms of Parkinson’s disease (PD) patients using digitised spiraldrawing tasks. Two spiral drawings, namely a circular and a square spiral, are designed to, respectively, represent the continuous and discrete arm motions, and the size of the spiral is decided so that both the distal and proximal arm joints are involved. Fifteen PD patients, average disease duration 14.4 ± 7.4 years, were assessed 30 min after a levodopa challenge whilst performing circular and square spiral-drawing tasks. The velocity of drawing movements was computed and the amplitude of the involuntary dyskinetic movements was measured as the standard deviation of the drawing velocity (SD-DV). The mean amplitude of dyskinetic movements was compared between arms and tasks and was correlated with clinical measures including the Bain dyskinesia scale and the total unified Parkinson’s disease rating scale (UPDRS) score. The results showed that there was no statistically significant difference in the amplitude of dyskinesias either between the arms or between the continuous (circular) and discrete (square) spiral drawings in this group of PD patients, but interestingly the interaction between arm and drawing pattern was significant. Significant correlations were found between the magnitude of dyskinesia measured from the spiral-drawing tasks and both the ‘on’ or ‘off’ UPDRS and also the Bain dyskinesia scale. We conclude that the drawing tasks may be used to provide an objective method of quantifying the severity of drug-induced dyskinesias in the arm in PD patients. © 2004 Elsevier B.V. All rights reserved.
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Title: A Review of Parkinson's Disease Cardinal and Dyskinetic Motor Symptoms Assessment Methods Using Sensor Systems

Section 1: Introduction
Description 1: Provide an overview of Parkinson's disease (PD), the importance of assessing motor symptoms, and the role of electronic sensor systems in these assessments.

Section 2: Parkinson's Disease Cardinal and Dyskinetic Symptoms
Description 2: Describe the main motor symptoms of PD, including tremor, rigidity, bradykinesia, postural instability, and dyskinesia.

Section 3: Sensors, Signals and Measures
Description 3: Discuss the various sensor systems and methods used to measure and analyze PD symptoms, including types of sensors, data processing techniques, and the role of machine learning.

Section 4: Symptom Assessment Using Different Sensor Technologies
Description 4: Detailed review of specific sensors like accelerometers, gyroscopes, electromyographs, and other wearable and non-wearable devices used in assessment of PD motor symptoms.

Section 5: Data Analysis and Machine Learning in Symptom Assessment
Description 5: Explain the different data analysis techniques, including wavelet transform, principal component analysis (PCA), and machine learning methods used in symptom assessment.

Section 6: Evaluating the Effectiveness of Sensor-Based Systems
Description 6: Review various studies that have evaluated the effectiveness of different sensor-based systems in accurately assessing PD motor symptoms.

Section 7: Comparisons and Limitations of Current Methods
Description 7: Compare the different sensor technologies and their effectiveness in assessing PD, along with the limitations and challenges inherent in current methods.

Section 8: Future Directions and Innovations
Description 8: Discuss potential future advancements in sensor technology, data analysis methods, and the role of artificial intelligence in improving PD motor symptom assessment.