SEMG-Based Hand Posture Recognition and Visual Feedback Training for the Forearm Amputee.

ABSTRACT: sEMG-based gesture recognition is useful for human-computer interactions, especially for technology supporting rehabilitation training and the control of electric prostheses. However, high variability in the sEMG signals of untrained users degrades the performance of gesture recognition algorithms. In this study, the hand posture recognition algorithm and radar plot-based visual feedback training were developed using multichannel sEMG sensors. Ten healthy adults and one bilateral forearm amputee participated by repeating twelve hand postures ten times. The visual feedback training was performed for two days and five days in healthy adults and a forearm amputee, respectively. Artificial neural network classifiers were trained with two types of feature vectors: a single feature vector and a

SUBMITTER: Kim J 

PROVIDER: S-EPMC9608765 | biostudies-literature | 2022 Oct

REPOSITORIES: biostudies-literature