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Detection of gait variations by using artificial neural networks

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Abstract

Walking is an everyday activity and contains variations from person to person, from one step to another step. The variation may occur due to the uniqueness of each gait cycle, personal parameters, such as age, walking speed, etc., and the existence of a gait abnormality. Understanding the normal variation depending on personal parameters helps medical experts to identify deviations from normal gait and engineers to design compatible orthotic and prosthetic products. In the present study, we aimed to obtain normal gait variations based on age, sex, height, weight, and walking speed. For this purpose, a large dataset of walking trials was used to model normal walking. An artificial neural network-based gait characterization model is proposed to show the relation between personal parameters and gait parameters. The neural network model simulates normal walking by considering the effect of personal parameters. The predicted behavior of gait parameters by artificial neural network model has a similarity with existing literature. The differences between experimental data and the neural network model were calculated. To determine how much deviation between predictions and experiments can be considered excessive, the distributions of differences for each gait parameter were obtained. The phases of walking in which excessive differences were intensified were determined. It was revealed that the artificial neural network-based gait characterization model exhibits the behavior of the normal gait parameters depending on the personal parameters.

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Acknowledgements

We would like to thank Dr. Hiroaki Hobara for his comments and the contributions on discussions.

Funding

The authors declare that no funds, grants, or other support were received during the preparation of this manuscript.

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Authors and Affiliations

  1. Department of Systems Innovation, School of Engineering, The University of Tokyo, Tokyo, Japan

    Cem Guzelbulut, Kazuo Yonekura & Katsuyuki Suzuki

  2. The KAITEKI Institute, Inc., Tokyo, Japan

    Satoshi Shimono

Authors
  1. Cem Guzelbulut
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  2. Satoshi Shimono
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  3. Kazuo Yonekura
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  4. Katsuyuki Suzuki
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Contributions

GC carried out the concept of the study, performed calculations and obtained results. SK and YK contributed to discussions of the method, model, and results. SS contributed to discussions. GC wrote the manuscript, SK, YK and SS read and reviewed the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Cem Guzelbulut.

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Competing interests

The authors declare that they have no competing interests.

Ethics approval

The study protocol of the publicly available AIST gait database was approved by the local ethical committee.

Informed consent

The written consent was obtained from all participants. (Source: https://unit.aist.go.jp/harc/ExPART/GDB2019.html).

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Guzelbulut, C., Shimono, S., Yonekura, K. et al. Detection of gait variations by using artificial neural networks. Biomed. Eng. Lett. 12, 369–379 (2022). https://doi.org/10.1007/s13534-022-00230-2

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  • DOI: https://doi.org/10.1007/s13534-022-00230-2

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