基于多层支持向量机的膝关节骨关节炎康复监测在线分割

Hsieh-Ping Chen, Hsieh-Chung Chen, Kai-Chun Liu, Chia-Tai Chan
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引用次数: 6

摘要

康复训练是膝关节骨关节炎治疗的重要组成部分之一。良好的康复监测方法为物理治疗师提供了对康复进展有很大帮助的绩效指标。由于人体运动的高度自由度,对运动剖面进行准确的在线分割是监测和分析的主要难点之一。提出了一种基于惯性传感器采集的康复训练数据的初始姿态分类和在线分割方法。具体来说,我们引入了基于阈值的初始姿态分类算法和用于在线分割的多层支持向量机(SVM)模型。该方法能够对运动数据进行准确的在线分割和分类。通过对10名膝关节骨性关节炎常见康复训练的受试者进行验证,第一层SVM的初始姿态分类准确率为97.9%,第二层SVM的分割准确率为90.6%,第二层SVM的分割准确率为92.7%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Online segmentation with multi-layer SVM for knee osteoarthritis rehabilitation monitoring
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.
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