Department of Physical Medicine and Rehabilitation, University of Texas Health Science Center, Houston, TX, United States of America. TIRR Memorial Hermann Research Center, Houston, TX, United States of America.
J Neural Eng. 2019 Jun;16(3):036018. doi: 10.1088/1741-2552/ab0cf0. Epub 2019 Mar 5.
The objective of this study was to investigate the feasibility of applying myoelectric pattern recognition for controlling a robotic hand in individuals with spinal cord injury (SCI).
Surface electromyogram (sEMG) signals of six hand motion patterns were recorded from 12 subjects with SCI. Online and offline classification performance of two classifiers (Gaussian Naive Bayes classifier, GNB, and support vector machine, SVM) were investigated. An exoskeleton hand was then controlled in real-time using the classification results. The control accuracy and its correlation with function assessments were investigated.
Average offline classification accuracy of all tested SCI subjects was (73.6 ± 14.0)% for GNB and (77.6 ± 11.6)% for SVM, respectively. Average online classification accuracy was significantly lower, (64.3 ± 15.0)% for GNB and (70.2 ± 13.2)% for SVM. Average control accuracy of (81.0 ± 16.3)% was achieved in real-time control of the robotic hand using myoelectric pattern recognition. Correlation between control accuracy and grip/pinch force was observed.
The results show that it is feasible to extract hand motion intent from individuals with SCI and control a robotic hand device using myoelectric pattern recognition. The performance of real-time control can be predicted based on functional assessments.
本研究旨在探讨肌电模式识别在脊髓损伤（SCI）患者控制机器人手方面的可行性。
从 12 名 SCI 受试者中记录了 6 种手部运动模式的表面肌电图（sEMG）信号。研究了两种分类器（高斯朴素贝叶斯分类器（GNB）和支持向量机（SVM））的在线和离线分类性能。然后，使用分类结果实时控制外骨骼手。研究了控制精度及其与功能评估的相关性。
所有测试的 SCI 受试者的离线分类平均准确率分别为 GNB（73.6±14.0）%和 SVM（77.6±11.6）%。在线分类平均准确率显著降低，分别为 GNB（64.3±15.0）%和 SVM（70.2±13.2）%。使用肌电模式识别实时控制机器人手，平均控制精度为（81.0±16.3）%。观察到控制精度与握持/捏力之间的相关性。
结果表明，从 SCI 患者中提取手部运动意图并使用肌电模式识别控制机器人手装置是可行的。可以根据功能评估来预测实时控制的性能。
