Effects of non-training movements on the performance of motion classification in electromyography pattern recognition

In electromyography pattern-recognition-based control of a multifunctional prosthesis, it would be inevitable for the users to unintentionally perform some classes of movements that are excluded from the training motion classes of a classifier, which might decay the performance of a trained classifier. It remains unknown how these untrained movements, designated as non-target movements (NTMs) in the study, would affect the performance of a trained classifier in the control of multifunctional prostheses. The goal of the current study was to evaluate the effects of NTMs on the performance of movement classification. Five classes of target movements (TMs) and four classes of NTMs were considered in this pilot study. A classifier based on a linear discriminant analysis (LDA) was trained with the electromyography (EMG) signals from the five TMs and the effects of the four NTMs were examined by feeding the EMG signals of the four NTMs to the trained classifier. Our results showed that these NTMs were classified into one or more classes of the TMs, which would cause the unexpected movements of prostheses. A method to reduce the effects of NTMs has been proposed in the study and our results showed that the averaged classification accuracies of the corrected classifiers were above 99% for the healthy subjects.