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EMG Based Control of Transhumeral Prosthesis Using Machine Learning Algorithms

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  • Intelligent Control and Applications
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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.

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Authors and Affiliations

  1. Department of Mechatronics and Biomedical Engineering, Air University, Islamabad, Pakistan

    Neelum Yousaf Sattar, Zareena Kausar, Syed Ali Usama & Umer Farooq

  2. Department of Mechatronics Engineering, CEME, National University of Sciences and Technology, Islamabad, Pakistan

    Umar Shahbaz Khan

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  1. Neelum Yousaf Sattar
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Correspondence to Neelum Yousaf Sattar.

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Neelum Yousaf Sattar is pursuing her Ph.D. in mechatronics engineering. She received her M.S. degree in mechatronics engineering from Air University in 2015. Her research interests include Control theory and application, robotics and automation systems and bio-mechatronics.

Zareena Kausar received her Ph.D. degree in engineering from University of Auckland in 2013. Her research interests include dynamics modeling of mechatronics systems, non-linear control, biomechatronics, robotics, mechatronics system design.

Syed Ali Usama received his M.S. degree in mechatronics from the Air University, Islamabad, Pakistan in 2020. He is currently a Lab Engineer in teh Department of Mechatronics and Biomedical Engineering, Air University. His research areas include human-machine interface, assistive robotics, intelligent control, artificial intelligence, biomechatronic and neuroscience.

Umer Farooq has received his M.S. degree in computer engineering from LUMS. Currently he is working as a Lecturer in the Department of Mechatronics Engineering at Air University since 2013 with a keen interest in Industrial/Commercial Problem Solving & Research opportunities, and aspires to bridge the gap between Academia & Industry.

Umar Shahbaz Khan completed his Ph.D. in electrical engineering from University of Liverpool, UK in 2010. Currently he is working as an Assistant professor at the Department of Mechatronics Engineering, National University of Sciences and Technology and his research interests include electrical systems manufacturing. He is also the project director of National Centre of Robotics and Automation.

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Sattar, N.Y., Kausar, Z., Usama, S.A. et al. EMG Based Control of Transhumeral Prosthesis Using Machine Learning Algorithms. Int. J. Control Autom. Syst. 19, 3522–3532 (2021). https://doi.org/10.1007/s12555-019-1058-5

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