Motion recognition for simultaneous control of multifunctional transradial prostheses

Electromyography (EMG) pattern-recognition based control strategies for multifunctional myoelectric prosthesis systems have been studied commonly in a controlled laboratory setting. Most previous efforts concentrated on evaluating the performance of EMG pattern-recognition algorithms in identifying one signal movement at a time. Therefore, the current motion classification methods would be limited with the difficulties in identifying the combined upper-limb motion classes that are commonly required in performing activities daily. In this paper, four improved classifier training schemes were proposed and investigated to address the difficulties mentioned above. Our preliminary results showed that three of the four proposed training schemes could improve the classification performance. The average classification accuracies of the three methods were 75.10% ± 9.71%, 76.95% ± 8.02%, and 77.56% ± 6.55% for the able-bodied subjects, and 63.38% ± 7.51%, 62.55% ± 9.06%, and 62.50% ± 9.36% for the transradial amputees, respectively. These results suggested that the proposed methods could provide better classification performance in identifying the combined motions than the current methods.