Although Electromyography (EMG) signals are sources of neural information that are essential in controlling the prosthetic hand, many confounding factors caused the variation of EMG signals properties over time. These factors degraded the performance of myoelectric prosthesis and made it unstable over time, across subjects and sessions such as stress, fatigue, muscular dystrophy, shifting electrodes locations, etc. The spatial information of muscle activity can be augmented using high-density surface electromyography (HD-sEMG) electrodes technology. In this study, HD-sEMG electrodes technology are integrated with the robust hybrid spatial features to improve the performance of myoelectric prostheses towards the non-stationary characteristics of EMG signals over time and across sessions. Three types of spatial feature sets are proposed using histogram oriented gradient (HOG) algorithm and intensity features. Three sub databases are used for evaluating the SVM classifier based on the proposed features. Intra-session and inter-session evaluation in offline manner show the potential of the proposed feature sets to improve the classification performance. The classification performance based on hybrid spatial features achieved precision of 97.9 %, sensitivity of 97.5 % for intra-session evaluation and a classification accuracy about 92.18 % for inter-session evaluation. Online classification results exhibit the robustness of hybrid spatial features (i.e. it achieved a classification accuracy based on hybrid spatial features of 92 % for intra-session evaluation and 89.9 % between sessions). Further, reducing the sampling rate to a certain extent without affecting the classification accuracy indicates the robustness and reliability of the proposed features. The results confirm that the robust spatial features have a significant effect on the classification accuracy more than that of the classifier algorithm.

Online myoelectric pattern recognition based on hybrid spatial features

Fortuna L.
2021-01-01

Abstract

Although Electromyography (EMG) signals are sources of neural information that are essential in controlling the prosthetic hand, many confounding factors caused the variation of EMG signals properties over time. These factors degraded the performance of myoelectric prosthesis and made it unstable over time, across subjects and sessions such as stress, fatigue, muscular dystrophy, shifting electrodes locations, etc. The spatial information of muscle activity can be augmented using high-density surface electromyography (HD-sEMG) electrodes technology. In this study, HD-sEMG electrodes technology are integrated with the robust hybrid spatial features to improve the performance of myoelectric prostheses towards the non-stationary characteristics of EMG signals over time and across sessions. Three types of spatial feature sets are proposed using histogram oriented gradient (HOG) algorithm and intensity features. Three sub databases are used for evaluating the SVM classifier based on the proposed features. Intra-session and inter-session evaluation in offline manner show the potential of the proposed feature sets to improve the classification performance. The classification performance based on hybrid spatial features achieved precision of 97.9 %, sensitivity of 97.5 % for intra-session evaluation and a classification accuracy about 92.18 % for inter-session evaluation. Online classification results exhibit the robustness of hybrid spatial features (i.e. it achieved a classification accuracy based on hybrid spatial features of 92 % for intra-session evaluation and 89.9 % between sessions). Further, reducing the sampling rate to a certain extent without affecting the classification accuracy indicates the robustness and reliability of the proposed features. The results confirm that the robust spatial features have a significant effect on the classification accuracy more than that of the classifier algorithm.
2021
Classifier
Gesture recognition
HD-sEMG
Inter-session evaluation
Map
Myoelectric pattern recognition
Real time classification
Spatial features extraction
SVM
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.11769/558148
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