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Multimedia Technology and Enhanced Learning. 5th EAI International Conference, ICMTEL 2023, Leicester, UK, April 28-29, 2023, Proceedings, Part IV

Research Article

The Design of Rehabilitation Glove System Based on sEMG Signals Control

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  • @INPROCEEDINGS{10.1007/978-3-031-50580-5_21,
        author={Qing Cao and Mingxu Sun and Ruiyun Li and Yan Yan},
        title={The Design of Rehabilitation Glove System Based on sEMG Signals Control},
        proceedings={Multimedia Technology and Enhanced Learning. 5th EAI International Conference, ICMTEL 2023, Leicester, UK, April 28-29, 2023, Proceedings, Part IV},
        proceedings_a={ICMTEL PART 4},
        year={2024},
        month={2},
        keywords={Surface Electromyography(sEMG) Convolutional Neural Network (CNN) Pneumatic rehabilitation gloves Hand rehabilitation},
        doi={10.1007/978-3-031-50580-5_21}
    }
    
  • Qing Cao
    Mingxu Sun
    Ruiyun Li
    Yan Yan
    Year: 2024
    The Design of Rehabilitation Glove System Based on sEMG Signals Control
    ICMTEL PART 4
    Springer
    DOI: 10.1007/978-3-031-50580-5_21
Qing Cao1, Mingxu Sun1, Ruiyun Li1, Yan Yan2,*
  • 1: University of Jinan
  • 2: Shandong Guohe Industrial Technology Institute Co., Ltd.
*Contact email: yanyan@china-csi.com.cn

Abstract

Stroke is a sudden disorder that causes impaired blood circulation to the brain, and resulting in varying degrees of impairment of sensory and motor function of the hand. Rehabilitation gloves are devices that assist in the rehabilitation of the hand. The sEMG (Surface Electromyography) is a bioelectrical signal generated by muscle contraction. It is rich in physiological motor information and reflects the person's motor intention. That means sEMG signals is an ideal signal source for rehabilitation glove system. This paper describes the design of a rehabilitation glove system based on sEMG signals control. The system controls the movements of the rehabilitation glove by collecting and analyzing the sEMG signals, and is used to achieve the purpose of rehabilitation training. This system includes a rehabilitation glove system and a host computer. The rehabilitation glove system is used to control the rehabilitation glove to achieve rehabilitation movements, to perform rehabilitation training for patients and to collect sEMG signals. The host computer is used to receive signals and perform gesture classification by CNN (Convolutional Neural Network) to recognize the movement intention.

Keywords
Surface Electromyography(sEMG) Convolutional Neural Network (CNN) Pneumatic rehabilitation gloves Hand rehabilitation
Published
2024-02-21
Appears in
SpringerLink
http://dx.doi.org/10.1007/978-3-031-50580-5_21
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