A robust system that classifies various hand gestures would greatly help those using prosthetic limbs. Recently, emphasis has been placed on extracted features from the High Density - surface Electromyography (HD-sEMG) signals and the size of segmentation windows which augment the recognition accuracy. This paper proposes a hand gestures identification system, in which HD-sEMG signals are employed, and is supported by Force Myography (FMG) signals for 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), linear discriminant analysis (LDA), support vector machine SVM, and k-nearest neighbor (KNN), in which results showing that MEAN and RMS features are superior to others, while the best classifier is SVM. Several experiments have been achieved by the MATLAB platform to validate the proposed system, in which, a database of HD-sEMG signals comprising 65 isometric 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 system while the classification accuracy arrived at 99.1%.
In recent years, the number of researches in the field of artificial limbs has increased significantly in order to improve the performance of the use of these limbs by amputees. During this period, High-Density surface Electromyography (HD-sEMG) signals have been employed for hand gesture identification, in which the performance of the classification process can be improved by using robust spatial features extracted from HD-sEMG signals. In this paper, several algorithms of spatial feature extraction have been proposed to increase the accuracy of the SVM classifier, while the histogram oriented gradient (HOG) has been used to achieve this mission. So, several feature sets have been extracted from HD-sEMG signals such as; features extracted based on HOG denoted by (H); features have been generated by combine intensity feature with H features denoted as (HI); features have been generated by combine average intensity with H features denoted as (AIH). The proposed system has been simulated by MATLAB to calculate the accuracy of the classification process, in addition, the proposed system is practically validated in order to show the ability to use this system by amputees. The results show the high accuracy of the classifier in real-time which leads to an increase in the possibility of using this system as an artificial hand.