@inproceedings{2538217e3c9b47c18f4d4d79c2368700,
title = "Online segmentation with multi-layer SVM for knee osteoarthritis rehabilitation monitoring",
abstract = "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.",
keywords = "motion classification, multi-layer SVM, online segmentation, rehabilitation monitoring",
author = "Chen, {Hsieh Ping} and Chen, {Hsieh Chung} and Liu, {Kai Chun} and Chan, {Chia Tai}",
note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 13th Annual Body Sensor Networks Conference, BSN 2016 ; Conference date: 14-06-2016 Through 17-06-2016",
year = "2016",
month = jul,
day = "18",
doi = "10.1109/BSN.2016.7516232",
language = "English",
series = "BSN 2016 - 13th Annual Body Sensor Networks Conference",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "55--60",
booktitle = "BSN 2016 - 13th Annual Body Sensor Networks Conference",
address = "美國",
}