|EMG Based Control of Transhumeral Prosthesis Using Machine Learning Algorithms
Neelum Yousaf Sattar*, Zareena Kausar, Syed Ali Usama, Umer Farooq, and Umar Shahbaz Khan
International Journal of Control, Automation, and Systems, vol. 19, no. 10, pp.3522-3532, 2021
Abstract : This research presents work on control of a prosthetic arm using surface electromyography (sEMG) signals acquired from triceps and biceps of fifteen healthy and four amputated subjects. Myo armband was used to acquire sEMG signals corresponding to four different arm motions: elbow extension, elbow flexion, wrist pronation, and wrist supination. Ten time-domain features were extracted and considered for classification to recognize the
four-arm motions. These features and their various combinations were used to train four different classifiers, in both offline and real-time settings. It was found that the combination of signal mean and waveform length as a feature and k-nearest neighbors as classifier performed significantly better (p < 0.05) than all other combinations in both offline and real-time settings. The offline accuracies of 95.8% and 68.1% and real-time accuracies of 91.9% and 60.1% were obtained for healthy and amputated subjects, respectively. Results obtained using the presented scheme successfully demonstrate that using suitable features and classifier, classification accuracies can be significantly improved for transhumeral prosthesis, thereby, providing better, wearable and non-invasive control of prostheses using sEMG signals.
k-nearest neighbors, myo armband, prosthetic arm, real-time classification, surface electromyography, transhumeral amputation
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