Lower Limb Exo-Skeleton for Rehabilitation

Muhammad Moeed Zeb; Ali Maesam Kazmi; Dr. Wasif Muhammad; Zubair Mehmood; Muhammad Jehanzeb Irshad; Muhammad Waqas Jabbar; Nazam Siddique

Authors

Muhammad Moeed Zeb Intelligent Systems Laboratory, Department of Electrical Engineering, University of Gujrat, Gujrat, Pakistan.
Ali Maesam Kazmi Intelligent Systems Laboratory, Department of Electrical Engineering, University of Gujrat, Gujrat, Pakistan.
Dr. Wasif Muhammad Intelligent Systems Laboratory, Department of Electrical Engineering, University of Gujrat, Gujrat, Pakistan.
Zubair Mehmood Intelligent Systems Laboratory, Department of Electrical Engineering, University of Gujrat, Gujrat, Pakistan.
Muhammad Jehanzeb Irshad Intelligent Systems Laboratory, Department of Electrical Engineering, University of Gujrat, Gujrat, Pakistan.
Muhammad Waqas Jabbar Intelligent Systems Laboratory, Department of Electrical Engineering, University of Gujrat, Gujrat, Pakistan.
Nazam Siddique Intelligent Systems Laboratory, Department of Electrical Engineering, University of Gujrat, Gujrat, Pakistan.

Keywords:

Lower Limb Exoskeleton / Prosthesis, Non-Invasive Electromyography, Intention Recognition, Support Vector Machine

Abstract

Above-knee amputation remains a significant global issue, leaving many people physically disabled due to various natural and man-made causes, such as diseases, wars, and disasters. This article presents a novel, non-invasive active prosthesis based on electromyography (EMG). The proposed method offers a major advancement by achieving higher classification accuracy with minimal hardware requirements. Using EMG input signals, the active prosthesis controls three body postures: Sit, Stand, and Walk. These EMG signals are classified through two machine learning models: Support Vector Machine (SVM) and Long Short-Term Memory (LSTM) networks. Both models are evaluated based on accuracy. The results show that SVM outperforms LSTM, achieving a classification accuracy of 82%, while LSTM reaches 63%.

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