Design and Myoelectric Control of an Anthropomorphic Prosthetic Hand

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• 作者：Nianfeng Wang ; ^{menfwang@scut.edu.cn} ; Kunyi Lao ; Xianmin Zhang
• 关键词：electromyography ; anthropomorphic prosthetic hand ; myoelectric control ; pattern recognition ; prehensile gestures
• 刊名：Journal of Bionic Engineering
• 出版年：2017
• 出版时间：January 2017
• 年：2017
• 卷：14
• 期：1
• 页码：47-59
• 全文大小：3067 K
• 卷排序：14

文摘

This paper presents an anthropomorphic prosthetic hand using flexure hinges, which is controlled by the surface electromyography (sEMG) signals from 2 electrodes only. The prosthetic hand has compact structure with 5 fingers and 4 Degree of Freedoms (DoFs) driven by 4 independent actuators. Helical springs are used as elastic joints and the joints of each finger are coupled by tendons. The myoelectric control system which can classify 8 prehensile hand gestures is built. Pattern recognition is employed where Mean Absolute Value (MAV), Variance (VAR), the fourth-order Autoregressive (AR) coefficient and Sample Entropy (SE) are chosen as the optimal feature set and Linear Discriminant Analysis (LDA) is utilized to reduce the dimension. A decision of hand gestures is generated by LDA classifier after the current projected feature set and the previous one are “pre-smoothed”, and then the final decision is obtained when the current decision and previous decisions are “post-smoothed” from the decisions flow. The prosthetic hand can perform prehensile postures for activities of daily living and carry objects under the control of EMG signals.