1 Computer Engineering Dept., Basra University, Basra, Iraq.

2 Electrical Engineering Dept., Basra University, Basra, Iraq.


Electromyography signals (EMG) are an important source to infer
motion intention. It has been broadly applied in human-machine interfacing to
control the neurorehabilitation devices such as prosthesis and rehabilitation
robot. HD-sEMG is a muscle's activity recorded at the delimited area of the
skin using 2D array electrode. This strategy permits the analysis of sEMG
signals in both temporal and spatial domain. Recent studies display that the
spatial distribution of HD-EMG maps improves the recognition of tasks. This
work investigates the use of HD-EMG recording to control upper limb
prosthesis. The classification of eight hand gestures of able-bodied subjects was
developed. Three feature sets were presented in this work. HOG features, time
domain features(TD) and the combination of HOG and average intensity
features (AIH). Combination of features possibly improved the performance of
the classifier. Results show that the combined of intensity features and HOG
features achieved higher performance of classifier than other features
(Acc=99.37%, P=98.375%, S=97.5%)