Research on upper limb rehabilitation system based on visual feedback

Yu Song; Yi’nan Fu; Qi Li

doi:10.1117/12.2659860

30 November 2022 Research on upper limb rehabilitation system based on visual feedback

Yu Song, Yi’nan Fu, Qi Li

Proceedings Volume 12456, International Conference on Artificial Intelligence and Intelligent Information Processing (AIIIP 2022); 124562R (2022) https://doi.org/10.1117/12.2659860
Event: International Conference on Artificial Intelligence and Intelligent Information Processing (AIIIP 2022), 2022, Qingdao, China

Abstract

Patients with the loss of limb motor function often suffer from neurological damage of brain in the presence of normal limb neural pathways, resulting in reduced or lost limb movement. By observing the mirror image of the healthy limb movement, the relevant motor perception areas of the patient's brain can be stimulated and the motor and brain functions of the affected limb could also be remodeled. Based on the above theoretical support, this study designed a visual feedback upper limb rehabilitation system based on an attentional neural network, which analyzed and modelled the electromyography (EMG) of the upper limb through a new attentional neural network named MCAT-net. The proposed MCAT-net could determine the movement intention of the healthy upper limb with an accuracy about 91.04%. Further, based on MCAT-net, a visual feedback upper limb rehabilitation system is designed, which could receive and generate the augmented reality models of the affected upper limb for the patients who need the purpose of visual rehabilitating stimulation.

Citation Download Citation

Yu Song, Yi’nan Fu, and Qi Li "Research on upper limb rehabilitation system based on visual feedback", Proc. SPIE 12456, International Conference on Artificial Intelligence and Intelligent Information Processing (AIIIP 2022), 124562R (30 November 2022); https://doi.org/10.1117/12.2659860

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KEYWORDS

Visualization

Data modeling

Electromyography

Performance modeling

Statistical modeling

Neural networks

Visual process modeling

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