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Personalized gait trajectory generation based on anthropometric features using Random Forest

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Abstract

Using lower limb rehabilitation robots (LLRRs) to help stroke patients recover their walking ability is attracting more and more attention presently. Previous studies have shown that gait rehabilitation training with natural gait pattern can improve the therapeutic outputs. However, how to generate the personalized gait trajectory has not been well researched. In this paper, a personalized gait generation method based anthropometric features is proposed. Firstly, gait trajectories are fitted and simplified into Fourier coefficient vectors, which are used to represent gait trajectories. Secondly, fourteen body features are used to generate the personalized gait trajectories and the feature set is further optimized based on the minimal redundancy maximal relevance criterion for easy application on the LLRR. Then, the relationship between the optimized feature set and gait trajectories is modeled by using the RF algorithm. Finally, the performance of the proposed method is demonstrated by several comparison experiments.

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Acknowledgements

This research was supported by the National Natural Science Foundation of China (Grants 91848110, 61720106012, 91648208) and the Strategic Priority Research Program of Chinese Academy of Science (Grant No. XDB32000000).

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Authors and Affiliations

  1. The State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China

    Shixin Ren, Weiqun Wang, Zeng-Guang Hou, Xu Liang, Jiaxing Wang & Liang Peng

  2. University of Chinese Academy of Sciences, Beijing, 100049, China

    Shixin Ren, Weiqun Wang, Xu Liang, Jiaxing Wang & Liang Peng

  3. The CAS Center for Excellence in Brain Science and Intelligence Technology, Beijing, 100190, China

    Zeng-Guang Hou

  4. Institute of Artificial Intelligence and Robotics, Xi’an Jiaotong University, Xi’an, 710049, China

    Badong Chen

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  1. Shixin Ren
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Correspondence to Weiqun Wang.

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Ren, S., Wang, W., Hou, ZG. et al. Personalized gait trajectory generation based on anthropometric features using Random Forest. J Ambient Intell Human Comput 14, 15597–15608 (2023). https://doi.org/10.1007/s12652-019-01390-3

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  • DOI: https://doi.org/10.1007/s12652-019-01390-3

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