Abstract
Surface electromyography (sEMG) signals comprise electrophysiological information related to muscle activity. As this signal is easy to record, it is utilized to control several myoelectric prostheses devices. Several studies have been conducted to process sEMG signals more efficiently. However, research on optimal algorithms and electrode placements for the processing of sEMG signals is still inconclusive. In addition, very few studies have focused on minimizing the number of electrodes. In this study, we investigated the most effective method for myoelectric signal classification with a small number of electrodes. A total of 23 subjects participated in the study, and the sEMG data of 14 different hand movements of the subjects were acquired from targeted muscles and untargeted muscles. Furthermore, the study compared the classification accuracy of the sEMG data using discriminative feature-oriented dictionary learning (DFDL) and other conventional classifiers. DFDL demonstrated the highest classification accuracy among the classifiers, and its higher quality performance became more apparent as the number of channels decreased. The targeted method was superior to the untargeted method, particularly when classifying sEMG signals with DFDL. Therefore, it was concluded that the combination of the targeted method and the DFDL algorithm could classify myoelectric signals more effectively with a minimal number of channels.
Highlights
Upper limb amputations cause severe functional disability and significantly affect the daily lives of patients
The results showed that discriminative feature-oriented dictionary learning (DFDL) demonstrated the highest classification accuracy among all classifiers, and its performance was statistically significant except with respect to SVM_rbf
The classification rate determined using 4 channels was as high as that using 5 channels in both the targeted (p = 0.116) and untargeted methods (p = 0.551). These results suggest that classification accuracy can be maximized using DFDL, even with a smaller number of channels
Summary
Upper limb amputations cause severe functional disability and significantly affect the daily lives of patients. As a part of this effort, research on hand transplantation is underway; several problems must be resolved before this treatment can be applied widely, such as immunosuppressive complications and financial burdens [2] For this reason, a primary feasible treatment option for upper limb amputation is prostheses, which are used by approximately 80% of patients in daily life [3]. The passive prostheses are the most favorable, because they are low cost and easy to apply They are only intended for cosmetic purposes and do not provide any functional movements [4]. To overcome these limitations, myoelectric hand prostheses are currently being studied extensively. These prostheses are more functional than the passive prostheses and are more intuitive and feasible than body-powered and externally powered prostheses
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