Design of Hand Gesture Classification System Based on High Density-Surface Electromyography Accompanied Force Myography - Darweesh, Alya; Rashid, Mofeed (Iraqi Journal for Electrical and Electronic Engineering, 2025)
A robust system that classifies various hand gestures would greatly help those using prosthetic limbs. Recently, emphasishas been placed on extracted features from the High Density - surface Electromyography (HD-sEMG) signals andthe size of segmentation windows which augment the recognition accuracy. This paper proposes a hand gesturesidentification system, in which HD-sEMG signals are employed, and is supported by Force Myography (FMG) signalsfor this mission. Several feature types have been extracted from FMG and HD-sEMG signals such as MEAN, RMS,MAD, STD, and Variance, these features have been validated under some classifiers such as decision tree (DT), lineardiscriminant analysis (LDA), support vector machine SVM, and k-nearest neighbor (KNN), in which results showing thatMEAN and RMS features are superior to others, while the best classifier is SVM. Several experiments have been achievedby the MATLAB platform to validate the proposed system, in which, a database of HD-sEMG signals comprising 65isometric hand gestures is employed, where two (8×8) electrodes and 9 force sensors are used to collect the FMG data.This data was derived from 20 intact participants, the first preprocessing step was applied during the recording stage.Ten gestures are chosen to be classified from the 65 hand gestures. Results show the success of the proposed systemwhile the classification accuracy arrived at 99.1%.