SEMG-Based Hand Posture Recognition Considering Electrode Shift, Feature Vectors, and Posture Groups.

ABSTRACT: Surface electromyography (sEMG)-based gesture recognition systems provide the intuitive and accurate recognition of various gestures in human-computer interaction. In this study, an sEMG-based hand posture recognition algorithm was developed, considering three main problems: electrode shift, feature vectors, and posture groups. The sEMG signal was measured using an armband sensor with the electrode shift. An artificial neural network classifier was trained using 21 feature vectors for seven different posture groups. The inter-session and inter-feature Pearson correlation coefficients (PCCs) were calculated. The results indicate that the classification performance improved with the number of training sessions of the electrode shift. The number of sessions necessary for efficient training wa

SUBMITTER: Kim J 

PROVIDER: S-EPMC8624257 | biostudies-literature | 2021 Nov

REPOSITORIES: biostudies-literature