为可靠的肌电人机接口选择合适的手势
Selection of suitable hand gestures for reliable myoelectric human computer interface.
作者信息
Castro Maria Claudia F, Arjunan Sridhar P, Kumar Dinesh K
机构信息
Electrical Engineering Department, Centro Universitário da FEI, Av. Humberto de A. C. Branco, 3.972, São Bernardo do Campo, SP, 09850-901, Brazil.
Biosignal Lab., School of Electrical and Computer Engineering, RMIT University, GPO Box 2476, Melbourne, VIC, 3001, Australia.
出版信息
Biomed Eng Online. 2015 Apr 9;14:30. doi: 10.1186/s12938-015-0025-5.
BACKGROUND
Myoelectric controlled prosthetic hand requires machine based identification of hand gestures using surface electromyogram (sEMG) recorded from the forearm muscles. This study has observed that a sub-set of the hand gestures have to be selected for an accurate automated hand gesture recognition, and reports a method to select these gestures to maximize the sensitivity and specificity.
METHODS
Experiments were conducted where sEMG was recorded from the muscles of the forearm while subjects performed hand gestures and then was classified off-line. The performances of ten gestures were ranked using the proposed Positive-Negative Performance Measurement Index (PNM), generated by a series of confusion matrices.
RESULTS
When using all the ten gestures, the sensitivity and specificity was 80.0% and 97.8%. After ranking the gestures using the PNM, six gestures were selected and these gave sensitivity and specificity greater than 95% (96.5% and 99.3%); Hand open, Hand close, Little finger flexion, Ring finger flexion, Middle finger flexion and Thumb flexion.
CONCLUSION
This work has shown that reliable myoelectric based human computer interface systems require careful selection of the gestures that have to be recognized and without such selection, the reliability is poor.
背景
肌电控制的假手需要基于机器利用从前臂肌肉记录的表面肌电图（sEMG）来识别手势。本研究观察到，为实现准确的自动手势识别，必须选择一部分手势，并报告了一种选择这些手势以最大化灵敏度和特异性的方法。
方法
进行了实验，在受试者执行手势时从前臂肌肉记录sEMG，然后进行离线分类。使用由一系列混淆矩阵生成的提议的正负性能测量指数（PNM）对十种手势的性能进行排名。
结果
使用所有十种手势时，灵敏度和特异性分别为80.0%和97.8%。使用PNM对手势进行排名后，选择了六种手势，其灵敏度和特异性均大于95%（分别为96.5%和99.3%）；即手张开、手闭合、小指弯曲、无名指弯曲、中指弯曲和拇指弯曲。
结论
这项工作表明，可靠的基于肌电的人机接口系统需要仔细选择要识别的手势，否则可靠性较差。
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