Dexterous motion recognition for myoelectric control of multifunctional transradial prostheses

Abstract

Attributed to its superior dexterity, the human’s thumb plays a significant role in the activities of daily living (ADLs). In order to promote the operational capability of the prosthetic hand, state-of-the-art designs tend to integrate multiple degrees of freedom (DOFs) in thumb’s trapeziometacarpal (TM) joint for achieving versatile movements. However, without proper control methods, this fine dexterity of multi-DOF prosthetic hand is hard to be demonstrated. This problem becomes more serious when the control signals, electromyography (EMG), are highly limited on the residual stump of the amputee. Through experiments, the paper examines the feasibility of recognizing 4 thumb motions (flexion/extension and abduction/adduction) together with the other 12 finger- and wrist-related motions under a general surface EMG configuration. After surveying the classification accuracy of several selective motion groups (thumb motions, thumb-involved finger motions, thumb-involved wrist motions, etc.), we report here the current limitations of the prevalent pattern recognition-based EMG control schemes. Considering the severely impaired neuromuscular systems of the amputees, controlling those thumb motions for achieving dexterous operations still faces many challenges. We finally discuss several alternative strategies, including switching, extended physiological proprioception (EPP) and postural synergy, for dealing with this problem.