An sEMG Signal-based Robotic Arm for Rehabilitation applying Fuzzy Logic


  • Ngoc-Khoat Nguyen Faculty of Control and Automation, Electric Power University, Hanoi, Vietnam
  • Thi-Mai-Phuong Dao Faculty of Electrical Engineering, Hanoi University of Industry, Hanoi, Vietnam
  • Tien-Dung Nguyen Faculty of Control and Automation, Electric Power University, Hanoi, Vietnam
  • Duy-Trung Nguyen Faculty of Control and Automation, Electric Power University, Hanoi, Vietnam
  • Huu-Thang Nguyen Faculty of Electrical Engineering, Electronics and Refrigeration - Thanh Hoa College of Industry, Thanh Hoa, Vietnam
  • Van-Kien Nguyen Faculty of Electrical Engineering, Hanoi University of Industry, Hanoi, Vietnam
Volume: 14 | Issue: 3 | Pages: 14287-14294 | June 2024 |


The recent surge in biosignal-based control signifies a profound paradigm shift in biomedical engineering. This innovative approach has injected new life into control theory, ushering in advancements in human-body interaction and control. Surface Electromyography (sEMG) emerges as a pivotal biosignal, attracting considerable attention for its wide-ranging applications across medicine, science, and engineering, particularly in the domain of functional rehabilitation. This study delves into the use of sEMG signals for controlling a robotic arm, with the overarching aim of improving the quality of life for people with disabilities in Vietnam. Raw sEMG signals are acquired via appropriate sensors and subjected to a robust processing methodology involving analog-to-digital conversion, band-pass and low-pass filtering, and envelope detection. To demonstrate the efficacy of the processed sEMG signals, this study introduces a robotic arm model capable of mimicking intricate human finger movements. Employing a fuzzy logic control strategy, the robotic arm demonstrates successful operation in experimental trials, characterized by swift response times, thereby positioning it as a valuable assistive device for people with disabilities. This investigation not only validates the feasibility of sEMG-based control for robotic arms, but also underscores its potential to significantly improve the lives of individuals with disabilities, a demographic that represents a substantial portion (approximately 8%) of the Vietnamese population.


sEMG, robotic arm, digital signal processing, fuzzy logic control, rehabilitation


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How to Cite

N.-K. Nguyen, T.-M.-P. Dao, T.-D. Nguyen, D.-T. Nguyen, H.-T. Nguyen, and V.-K. Nguyen, “An sEMG Signal-based Robotic Arm for Rehabilitation applying Fuzzy Logic”, Eng. Technol. Appl. Sci. Res., vol. 14, no. 3, pp. 14287–14294, Jun. 2024.


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