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AI-driven rehabilitation and assistive robotic system with intelligent PID controller based on RBF neural networks

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Abstract

In this article, a cooperative bilateral upper-limb rehabilitation robotic system based on mirror therapy (MT) and virtual stimulation was developed to assist hemiplegia in rehabilitation training. The hemiplegia’s affected limb can be placed on one of the robotic arms with a servomotor, and the healthy limb is placed on another side without servomotor. With the assistance of the robotic arm, the affected limb can track the healthy limb to perform mirror motion to complete the rehabilitation training. The robotic arm device can be adjusted personally to assist the patient's elbow joint flexion and wrist joint rotation. In order to enhance the willingness of patients to actively recover. A game-based rehabilitation training was developed to realize human–computer interaction and to provide visual stimulation. The adaptive proportional–integral–derivative (PID) controller based on radial basis function (RBF) neural network has been adopted to improve the tracking performance of the affected side of robotic arm. The RBF neural network updates its parameters through the error signal between output of network and output of the system. The parameters of PID are updated by Jacobian matrix and the movement error between the healthy side and the affected side. Its abilities of RBF-PID controller about response speed, anti-interference and tracks are better than conventional PID controller’s through experimental validations. The system response was analyzed and graphed for different loading conditions. These error values of the angle of corresponding joint on both sides can be interpreted as very low. The system was proved to complete rehabilitation training and reflect the patient’s awareness of active rehabilitation.

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Acknowledgements

This work was supported in part by the National Natural Science Foundation of China (Grant No.61973220), the Natural Science Foundation of Guangdong Province (2021A1515012171), Shenzhen Municipal Scheme for Basic Research(JCYJ20180507182040213).

Funding

The National Natural Science Foundation of China (Grant No.61973220), The Natural Science Foundation of Guangdong Province (2021A1515012171).

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Author notes

  1. Wei Xiao and Kai Chen are co-first authors of article.

Authors and Affiliations

  1. School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China

    Wei Xiao, Kai Chen, Jiaming Fan, Weifei Kong & Guo Dan

  2. Hefei Second People’s Hospital, Hefei, China

    Yifan Hou

  3. Shenzhen Institute of Neuroscience, Shenzhen, China

    Guo Dan

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  1. Wei Xiao
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Contributions

All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Kai Chen, Jiaming Fan, Yifan Hou, Weifei Kong. The first draft of the manuscript was written by Wei Xiao. Wei Xiao and Kai Chen contributed equally to this work and should be considered co-first authors. Writing—review and editing, was performed by Guo Dan, and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Correspondence to Guo Dan.

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Xiao, W., Chen, K., Fan, J. et al. AI-driven rehabilitation and assistive robotic system with intelligent PID controller based on RBF neural networks. Neural Comput & Applic 35, 16021–16035 (2023). https://doi.org/10.1007/s00521-021-06785-y

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