康复
计算机科学
人工智能
冲程(发动机)
过程(计算)
机器学习
物理医学与康复
模式识别(心理学)
物理疗法
医学
工程类
机械工程
操作系统
作者
Fengyan Wang,Daohui Zhang,Shaokang Hu,Bo Zhu,Fei Han,Xingang Zhao
标识
DOI:10.1109/embc44109.2020.9175285
摘要
Rehabilitation level evaluation is an important part of the automatic rehabilitation training system. As a general rule, this process is manually performed by rehabilitation doctors using chart-based ordinal scales which can be both subjective and inefficient. In this paper, a novel approach based on ensemble learning is proposed which automatically evaluates stroke patients' rehabilitation level using multi-channel sEMG signals to this problem. The correlation between rehabilitation levels and rehabilitation training actions is investigated and actions suitable for rehabilitation assessment are selected. Then, features are extracted from the selected actions. Finally, the features are used to train the stacking classification model. Experiments using sEMG data collected from 24 stroke patients have been carried out to examine the validity and feasibility of the proposed method. The experiment results show that the algorithm proposed in this paper can improve the classification accuracy of 6 Brunnstrom stages to 94.36%, which can promote the application of home-based rehabilitation training in practice.
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