Decoding of Individuated Finger Movements Using Surface Electromyography

Upper limb prostheses are increasingly resembling the limbs they seek to replace in both form and functionality, including the design and development of multifingered hands and wrists. Hence, it becomes necessary to control large numbers of degrees of freedom (DOFs), required for individuated finger movements, preferably using noninvasive signals. While existing control paradigms are typically used to drive a single-DOF hook-based configurations, dexterous tasks such as individual finger movements would require more elaborate control schemes. We show that it is possible to decode individual flexion and extension movements of each finger (ten movements) with greater than 90% accuracy in a transradial amputee using only noninvasive surface myoelectric signals. Further, comparison of decoding accuracy from a transradial amputee and able-bodied subjects shows no statistically significant difference ( p < 0.05) between these subjects. These results are encouraging for the development of real-time control strategies based on the surface myoelectric signal to control dexterous prosthetic hands.

I. Introduction

Advances in upper limb prosthetics have typically lagged behind their lower limb counterparts. It is easy to identify two main reasons behind this “bias”: 1) the lower number of upper limb amputations implies a lower market demand for the prostheses [1],[2], making research and development of next-generation prostheses harder to justify; 2) the significantly larger number of actuated degrees of freedom (DOFs) involved in controlling the upper extremities requires many more biosignals (even compared to 0, such as in passive prostheses [3]) for a biomimetic control of the prostheses.