J Appl Biomed 15:282-290, 2017 | DOI: 10.1016/j.jab.2017.05.002

Segmentation and detection of physical activities during a sitting task in Parkinson's disease participants using multiple inertial sensors

Sara Memar^a,*, Mehdi Delrobaei^b, Greydon Gilmore^a, Kenneth McIsaac^c, Mandar Jog^a,d

^a Lawson Health Research Institute, London, ON, Canada

^b K. N. Toosi University of Technology, Faculty of Electrical Engineering, Tehran, Iran

^c Western University, Department of Electrical and Computer Engineering, London, ON, Canada

^d Western University, Department of Clinical Neurological Sciences, London, ON, Canada

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.

Keywords: Parkinson's disease; Machine learning; Activity detection; Auto segmentation

Received: February 17, 2017; Revised: April 27, 2017; Accepted: May 24, 2017; Published: November 1, 2017  Show citation