IEEE Trans Biomed Eng. 2019 Feb;66(2):365-372. doi: 10.1109/TBME.2018.2840848. Epub 2018 May 25.
Myoelectric pattern recognition has been successfully applied as a human-machine interface to control robotic devices such as prostheses and exoskeletons, significantly improving the dexterity of myoelectric control. This study investigates the feasibility of applying myoelectric pattern recognition for controlling a robotic hand in stroke patients.
Myoelectric pattern recognition of six hand motion patterns was performed using forearm electromyogram signals in paretic side of eight stroke subjects. Both the random cross validation (RCV) and the chronological handout validation (CHV) were applied to assess the offline myoelectric pattern recognition performance. Experiments on real-time myoelectric pattern recognition control of an exoskeleton robotic hand were also performed.
An average classification accuracy of 84.1% (the mean value from two different classifiers) and individual subject differences were observed in the offline myoelectric pattern recognition analysis using the RCV, while the accuracy decreased to 65.7% when the CHV was used. The stroke subjects achieved an average accuracy of 61.3 ± 20.9% for controlling the robotic hand. However, our study did not reveal a clear correlation between the real-time control accuracy and the offline myoelectric pattern recognition performance, or any specific characteristics of the stroke subjects.
The findings suggest that it is feasible to apply myoelectric pattern recognition to control the robotic hand in some but not all of the stroke patients. Each stroke subject should be individually online tested for the feasibility of applying myoelectric pattern recognition control for robot-assisted rehabilitation.
肌电模式识别已成功应用于假肢和外骨骼等机器人设备的人机接口控制，显著提高了肌电控制的灵活性。本研究探讨了将肌电模式识别应用于控制脑卒中患者手部机器人的可行性。
使用 8 名脑卒中患者患侧前臂肌电信号，对 6 种手部运动模式的肌电模式进行识别。采用随机交叉验证（RCV）和时序输出验证（CHV）评估离线肌电模式识别性能。还进行了外骨骼机器人手实时肌电模式识别控制实验。
RCV 下的离线肌电模式识别分析显示，平均分类准确率为 84.1%（两个不同分类器的平均值），个体受试者间存在差异，而 CHV 下的准确率下降至 65.7%。脑卒中患者对机器人手的平均控制准确率为 61.3±20.9%。然而，我们的研究并未揭示实时控制精度与离线肌电模式识别性能之间的明确相关性，也未揭示脑卒中患者的任何特定特征。
研究结果表明，肌电模式识别可应用于控制部分而非所有脑卒中患者的手部机器人。对于应用肌电模式识别控制机器人辅助康复的可行性，每个脑卒中患者都应进行在线个体测试。
