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Design and Myoelectric Control of an Anthropomorphic Prosthetic Hand

Nianfeng Wang, Kunyi Lao, Xianmin Zhang
  

  1. Guangdong Province Key Laboratory of Precision Equipment and Manufacturing Technology, South China University of Technology, Guangzhou 510640, China
  • 收稿日期:2016-06-05 修回日期:2016-12-05 出版日期:2017-01-10 发布日期:2017-01-05
  • 通讯作者: Nianfeng Wang E-mail:menfwang@scut.edu.cn
  • 作者简介:Nianfeng Wang, Kunyi Lao, Xianmin Zhang

Design and Myoelectric Control of an Anthropomorphic Prosthetic Hand

Nianfeng Wang, Kunyi Lao, Xianmin Zhang#br#   

  1. Guangdong Province Key Laboratory of Precision Equipment and Manufacturing Technology, South China University of Technology, Guangzhou 510640, China
  • Received:2016-06-05 Revised:2016-12-05 Online:2017-01-10 Published:2017-01-05
  • Contact: Nianfeng Wang E-mail:menfwang@scut.edu.cn
  • About author:Nianfeng Wang, Kunyi Lao, Xianmin Zhang

摘要: 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.

关键词: electromyography, anthropomorphic prosthetic hand, myoelectric control, pattern recognition, prehensile gestures

Abstract: 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.

Key words: electromyography, anthropomorphic prosthetic hand, myoelectric control, pattern recognition, prehensile gestures