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 ...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.展开更多
基金This work is supported by National Natural Science Foundation of China （Grant Nos. 51575187 and 91223201）, Science and Technology Program of Guangzhou （Grant No. 2014Y2-00217）, Science and Technology Major Project of Huangpu District of Guang-Zhou （Grant No, 20150000661）, the Fundamental Research Funds for the Central University （Grant No. 2015ZZ007） and Natural Science Foundation of Guangdong Province （Grant No. S2013030013355）.
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.
