Introduction. The development of inertial sensors in motion capture systems enables precise measurement of motor symptoms in Parkinson's disease (PD). The type of physical activities performed by the PD participants is an important factor to compute objective scores for specific motor symptoms of the disease. The goal of this study is to propose an approach to automatically detect the physical activities over a period time and segment the time stamps for such detected activities. Methods. A wearable motion capture sensor system using inertial measurement units (IMUs) was used for data collection. Data from the sensors attached to the shoulders, elbows, and wrists were utilized for detecting and segmenting the activities. An unsupervised machine learning algorithm was employed to extract suitable features from the appropriate sensors and classify the data points to the corresponding activity group. Results. The performance of the proposed technique was evaluated with respect to the manually labeled and segmented activities. The experimental results reveal that the proposed auto detection technique – by obtaining high average scores of accuracy (96%), precision (96%), and recall (98%) – is able to effectively detect the activities during the sitting task and segment them to the proper time stamps.

K N Toosi University of Technology Faculty of Electrical Engineering Tehran Iran

Lawson Health Research Institute London ON Canada

Western University Department of Clinical Neurological Sciences London ON Canada

Western University Department of Electrical and Computer Engineering London ON Canada

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