Online segmentation with multi-layer SVM for knee osteoarthritis rehabilitation monitoring

Hsieh Ping Chen, Hsieh Chung Chen, Kai Chun Liu, Chia Tai Chan

研究成果: Conference contribution同行評審

5 引文 斯高帕斯(Scopus)

摘要

Rehabilitation exercise is one of the most important parts in knee osteoarthritis therapy. A good rehabilitation monitoring method provides physiotherapists with performance metrics that are greatly helpful in recovery progress. One of the main difficulties of monitoring and analysis is performing accurate online segmentation of motion sections due to the high degree of freedom (DoF) of human motion. This paper proposes an approach for initial posture classification and online segmentation of rehabilitation exercise data acquired with body-worn inertial sensors. Specifically, we introduce a threshold-based algorithm for initial posture classification and a multi-layer Support Vector Machine (SVM) model for online segmentation. The proposed approach is capable of accurate online segmentation and classification of exercise data. The approach is verified on 10 subjects performing common rehabilitation exercises for knee osteoarthritis, giving initial posture classification accuracy of 97.9% and segmentation accuracy of 90.6% on layer-1 SVM and 92.7% on layer-2 SVM.

原文English
主出版物標題BSN 2016 - 13th Annual Body Sensor Networks Conference
發行者Institute of Electrical and Electronics Engineers Inc.
頁面55-60
頁數6
ISBN(電子)9781509030873
DOIs
出版狀態Published - 18 7月 2016
事件13th Annual Body Sensor Networks Conference, BSN 2016 - San Francisco, United States
持續時間: 14 6月 201617 6月 2016

出版系列

名字BSN 2016 - 13th Annual Body Sensor Networks Conference

Conference

Conference13th Annual Body Sensor Networks Conference, BSN 2016
國家/地區United States
城市San Francisco
期間14/06/1617/06/16

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