Real-time simultaneous myoelectric control by transradial amputees using linear and probability-weighted regression.

Abstract

Regression-based prosthesis control using surface electromyography (EMG) has demonstrated real-time simultaneous control of multiple degrees of freedom (DOFs) in transradial amputees. However, these systems have been limited to control of wrist DOFs. Use of intramuscular EMG has shown promise for both wrist and hand control in able-bodied subjects, but to date has not been evaluated in amputee subjects. The objective of this study was to evaluate two regression-based simultaneous control methods using intramuscular EMG in transradial amputees and compare their performance to able-bodied subjects. Two transradial amputees and sixteen able-bodied subjects used fine wire EMG recorded from six forearm muscles to control three wrist/hand DOFs: wrist rotation, wrist flexion/extension, and hand open/close. Both linear regression and probability-weighted regression systems were evaluated in a virtual Fitts' Law test. Though both amputee subjects initially produced worse performance metrics than the able-bodied subjects, the amputee subject who completed multiple experimental blocks of the Fitts' law task demonstrated substantial learning. This subject's performance was within the range of able-bodied subjects by the end of the experiment. Both amputee subjects also showed improved performance when using probability-weighted regression for targets requiring use of only one DOF, and mirrored statistically significant differences observed with able-bodied subjects. These results indicate that amputee subjects may require more learning to achieve similar performance metrics as able-bodied subjects. These results also demonstrate that comparative findings between linear and probability-weighted regression with able-bodied subjects reflect performance differences when used by the amputee population.