A Computerized Recognition System for the Home-Based Physiotherapy Exercises Using an RGBD Camera
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Date
2014
Authors
Ar, İlktan
Akgül, Yusuf Sinan
Journal Title
Journal ISSN
Volume Title
Publisher
IEEE
Abstract
Computerized recognition of the home based physiotherapy exercises has many benefits and it has attracted considerable interest among the computer vision community. However most methods in the literature view this task as a special case of motion recognition. In contrast we propose to employ the three main components of a physiotherapy exercise (the motion patterns the stance knowledge and the exercise object) as different recognition tasks and embed them separately into the recognition system. The low level information about each component is gathered using machine learning methods. Then we use a generative Bayesian network to recognize the exercise types by combining the information from these sources at an abstract level which takes the advantage of domain knowledge for a more robust system. Finally a novel postprocessing step is employed to estimate the exercise repetitions counts. The performance evaluation of the system is conducted with a new dataset which contains RGB (red green and blue) and depth videos of home-based exercise sessions for commonly applied shoulder and knee exercises. The proposed system works without any body-part segmentation bodypart tracking joint detection and temporal segmentation methods. In the end favorable exercise recognition rates and encouraging results on the estimation of repetition counts are obtained.
Description
Keywords
Bayesian network, Estimation of repetition count, Exercise recognition, Home-based physiotherapy
Turkish CoHE Thesis Center URL
Citation
34
WoS Q
Q1
Scopus Q
Q1
Source
Volume
22
Issue
6
Start Page
1160
End Page
1171