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