Signals from electromyography (EMG) are used to infer the user's motor goals. It has been haphazardly used in HMI to manage neuro-rehabilitation tools like prosthetics and therapy machines. High-Definition Surface Electromyography (HD-sEMG) could be the recording of muscle activity at a defined skin area using a second array conductor. This method enables the spatial and temporal analysis of EMG data. New studies have indicated that the requirement for these kinds of analyses grows as the geographic range of HD-EMG maps expands. In this study, HD-EMG recording is examined for its potential application in controlling prosthetic devices for the upper limb. For this study, we categorized eight different fitness-related hand gestures. Throughout this process, we consulted three different feature sets.Options for histogram-oriented gradients (HOG), choices for working in the time domain (TD), and, by extension, the clustering of HOGs and Average Intensity Hysteresis (AIH).The classifier's performance was most likely elevated when many options were combined.

Myoelectric, Spatial features, histogram.