Pattern recognition control of multifunction myoelectric prostheses by patients with congenital transradial limb defects: a preliminary study.

Abstract

Electromyography (EMG) pattern recognition offers the potential for improved control of multifunction myoelectric prostheses. However, it is unclear whether this technology can be successfully used by congenital amputees. The purpose of this investigation was to assess the ability of congenital transradial amputees to control a virtual multifunction prosthesis using EMG pattern recognition and compare their performance to that of acquired amputees from a previous study. Preliminary cross-sectional study. Four congenital transradial amputees trained and tested a linear discriminant analysis (LDA) classifier with four wrist movements, five hand movements, and a no-movement class. Subjects then tested the classifier in real time using a virtual arm. Performance metrics for the residual limb were poorer than those with the intact limb (classification accuracy: 52.1% ± 15.0% vs. 93.2% ± 15.8%; motion-completion rate: 49.0%± 23.0% vs. 84.0% ± 9.4%; motion-completion time: 2.05 ± 0.75 s vs. 1.13 ± 0.05 s, respectively). On average, performance with the residual limb by congenital amputees was reduced compared to that reported for acquired transradial amputees. However, one subject performed similarly to acquired amputees. Pattern recognition control may be a viable option for some congenital amputees. Further study is warranted to determine success factors.