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Personalized passive training control strategy for a lower limb rehabilitation robot with specified step lengths

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Abstract

Passive training is a crucial rehabilitation strategy in lower limb rehabilitation robotics. However, most rehabilitation robots currently use a fixed training mode, which cannot be personalized according to the individual characteristics and rehabilitation needs of patients. To increase training effectiveness and improve the rehabilitation experience, this study introduces a personalized passive training control strategy for a rehabilitation robot, focusing on gait training in early-stage rehabilitation for patients with lower limb dysfunction. First, a personalized gait trajectory generation model is developed based on radial basis function neural networks (RBFNN). This model aims to generate personalized gait trajectories to accommodate different patients with varying step lengths. A proportional-integral-derivative (PID) controller based on the motor speed loop is subsequently implemented to enable the robot to accurately track the generated personalized gait trajectories. To evaluate the effectiveness of the control strategy, five healthy participants were recruited for experimental verification. The research findings demonstrate that the model based on the RBFNN can generate gait patterns with specified step lengths with high precision and accuracy. Compared with the previous state-of-the-art Gaussian process regression (GPR) method, this model reduces the prediction error of lower limb joint angles and center of gravity (CG) trajectories by 11.34% and 6.49%, respectively. Moreover, the control performance of the PID controller is verified through experimental comparisons and analysis of literature results. Therefore, this control strategy can effectively coordinate the control of the ankle, knee, and hip joints, as well as the CG, and achieve personalized gait rehabilitation training for specific step lengths.

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Availability of data and materials

The relevant gait data and code can be provided from the corresponding author upon reasonable request.

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Funding

This work was supported in part by the National Natural Science Foundation of China (Grant No. 52075177), the National Key Research and Development Program of China (Grant No. 2021YFB3301400), and the Research Foundation of Guangdong Province (Grant No. 2019A050505001).

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Authors and Affiliations

  1. Shien-Ming Wu School of Intelligent Engineering, South China University of Technology, Guangzhou, 511442, China

    Shuoyu Li, Chupeng Su, Shuangyuan Huang & Longhan Xie

  2. Rehabilitation Department, The Third Affiliated Hospital of SUN YAT, SEM University Lingnan Hospital, Guangzhou, 510000, China

    Li Huang

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  1. Shuoyu Li
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  4. Shuangyuan Huang
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Correspondence to Longhan Xie.

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The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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The ethical approval and informed consent of all participants have been obtained, and the Ethics Committee of the Guangzhou First People’s Hospital Department has approved all ethical and experimental procedures for the research. All studies strictly adhered to the relevant provisions of the Helsinki Declaration.

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Li, S., Su, C., Huang, L. et al. Personalized passive training control strategy for a lower limb rehabilitation robot with specified step lengths. Intel Serv Robotics 18, 137–156 (2025). https://doi.org/10.1007/s11370-024-00576-9

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