Abstract
Upper limb rehabilitation training is an important method for stroke patients with hemiplegia to restore their upper limb motor ability. The combination of virtual reality (VR) technology and rehabilitation training can increase the effectiveness and interest of the training process. In the process of virtual reality rehabilitation training, dynamic uncertainty factors will affect the effectiveness of rehabilitation training, so it is necessary to adjust the decision of rehabilitation training in real time according to the status of patients. In this paper, a virtual reality upper limb rehabilitation training game with controllable difficulty parameters is designed to collect the electromyographic (EMG) signals and motion signal of patients in the process of movement. Through the fuzzy neural network rehabilitation training decision-making method optimized based on the cuckoo algorithm, the control parameters of virtual reality rehabilitation training scene are adjusted to make the difficulty of rehabilitation training task match the upper limb movement ability of patients adaptively. We recruited 23 stroke patients with different stages of Brunnstrom rehabilitation participated in this experiment. The accuracy of the rehabilitation training decision-making method has an accuracy rate of 96.23%. It can accurately make rehabilitation training decisions, adjust the difficulty of training tasks to a challenging but feasible level, and improve the rehabilitation training effect.
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Acknowledgments
This work is supported by the National Natural Science Foundation of China under Grant 52075398 and the Research Project of Wuhan University of Technology Chongqing Research Institute under Grant YF2021-17.
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Zhang, J., Liu, Y., Liu, J. (2022). Wearable Sensing Based Virtual Reality Rehabilitation Scheme for Upper Limb Training. In: Liu, H., et al. Intelligent Robotics and Applications. ICIRA 2022. Lecture Notes in Computer Science(), vol 13457. Springer, Cham. https://doi.org/10.1007/978-3-031-13835-5_3
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