The BioRobotics Institute (formerly ARTS and CRIM Labs), Scuola Superiore Sant’Anna, 56025 Pontedera, Italy.
IEEE Trans Neural Syst Rehabil Eng. 2011 Jun;19(3):260-70. doi: 10.1109/TNSRE.2011.2108667. Epub 2011 Jan 31.
A real-time pattern recognition algorithm based on k-nearest neighbors and lazy learning was used to classify, voluntary electromyography (EMG) signals and to simultaneously control movements of a dexterous artificial hand. EMG signals were superficially recorded by eight pairs of electrodes from the stumps of five transradial amputees and forearms of five able-bodied participants and used online to control a robot hand. Seven finger movements (not involving the wrist) were investigated in this study. The first objective was to understand whether and to which extent it is possible to control continuously and in real-time, the finger postures of a prosthetic hand, using superficial EMG, and a practical classifier, also taking advantage of the direct visual feedback of the moving hand. The second objective was to calculate statistical differences in the performance between participants and groups, thereby assessing the general applicability of the proposed method. The average accuracy of the classifier was 79% for amputees and 89% for able-bodied participants. Statistical analysis of the data revealed a difference in control accuracy based on the aetiology of amputation, type of prostheses regularly used and also between able-bodied participants and amputees. These results are encouraging for the development of noninvasive EMG interfaces for the control of dexterous prostheses.
基于 k-最近邻和懒惰学习的实时模式识别算法被用于分类、自愿肌电图 (EMG) 信号，并同时控制灵巧的假手运动。通过五名桡骨截肢者残肢和五名健全参与者前臂上的 8 对电极进行表面记录 EMG 信号，并在线用于控制机器人手。本研究调查了 7 个手指运动（不涉及手腕）。第一个目标是了解是否以及在何种程度上可以使用表面 EMG 和实用分类器实时连续控制假肢手的手指姿势，同时还利用移动手的直接视觉反馈。第二个目标是计算参与者和组之间性能的统计差异，从而评估所提出方法的通用性。分类器的平均准确率为 79%的截肢者和 89%的健全参与者。对数据的统计分析表明，基于截肢的病因、常规使用的假肢类型以及健全参与者和截肢者之间存在控制精度的差异。这些结果令人鼓舞，为控制灵巧假肢的非侵入性 EMG 接口的发展提供了支持。
