Wang Fengyan, Zhang Daohui, Hu Shaokang, Zhu Bo, Han Fei, Zhao Xingang
Annu Int Conf IEEE Eng Med Biol Soc. 2020 Jul;2020:3763-3766. doi: 10.1109/EMBC44109.2020.9175285.
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.
康复水平评估是自动康复训练系统的重要组成部分。一般来说，这个过程由康复医生手动执行，使用基于图表的有序量表，这可能既主观又低效。本文提出了一种基于集成学习的新方法，该方法使用多通道表面肌电信号自动评估中风患者的康复水平以解决此问题。研究了康复水平与康复训练动作之间的相关性，并选择适合康复评估的动作。然后，从选定的动作中提取特征。最后，使用这些特征训练堆叠分类模型。利用从24名中风患者收集的表面肌电数据进行了实验，以检验所提方法的有效性和可行性。实验结果表明，本文提出的算法可将6个Brunnstrom阶段的分类准确率提高到94.36%，这可以促进家庭康复训练在实际中的应用。
