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.
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Cao, Q., Sun, M., Li, R., Yan, Y. (2024). The Design of Rehabilitation Glove System Based on sEMG Signals Control. In: Wang, B., Hu, Z., Jiang, X., Zhang, YD. (eds) Multimedia Technology and Enhanced Learning. ICMTEL 2023. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 535. Springer, Cham. https://doi.org/10.1007/978-3-031-50580-5_21
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DOI: https://doi.org/10.1007/978-3-031-50580-5_21
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