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2025 | OriginalPaper | Buchkapitel

Identifying Human Movement Patterns: Multivariate Gait Analysis Through Machine Learning

verfasst von : Raunak Kumar, Usha Mittal, Priyanka Chawla

Erschienen in: Proceedings of Third International Conference on Computational Electronics for Wireless Communications

Verlag: Springer Nature Singapore

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Abstract

The ability to identify walking conditions correctly is essential for both diagnosing and treating gait abnormalities. This study utilized machine learning algorithms to analyze multivariate gait data obtained from 10 healthy participants walking under three distinct settings, including normal treadmill walking while wearing an ankle brace on the right. In order to increase the precision and efficiency of the ML models, the authors adopted a pipeline technique to handle the data. This research unveils the preeminence of random forest, achieving an impressive (92%) accuracy, surpassing logistic regression, neural network, naive Bayes, and perceptron. It exemplifies the formidable potential of machine learning algorithms for gait classification. The application of the results may be restricted to the particular dataset and walking conditions employed in the study, and the proposed study’s limitations include the use of a constrained number of algorithms and hyperparameter tuning settings. The proposed study has implications for the design of diagnostic tools and assistive devices for people with gait abnormalities and emphasizes the value of paying close attention to hyperparameters and other important model parameters to achieve the highest level of accuracy and performance in machine learning models. Future studies might build on this strategy by utilizing more datasets, additional algorithms, and sophisticated optimization methods to boost the precision of gait categorization.

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Literatur
2.
Zurück zum Zitat Wu J, Maurenbrecher H, Schaer A, Becsek B, Awai Easthope C, Chatzipirpiridis G et al (2021) Human gait-labeling uncertainty and a hybrid model for gait segmentation. TechRxiv Wu J, Maurenbrecher H, Schaer A, Becsek B, Awai Easthope C, Chatzipirpiridis G et al (2021) Human gait-labeling uncertainty and a hybrid model for gait segmentation. TechRxiv
5.
Zurück zum Zitat Pinyoanuntapong E, Ali A, Wang P, Lee M, Chen C (2022) GaitMixer: skeleton-based gait representation learning via wide-spectrum multi-axial mixer. arXiv preprint arXiv:2210.15491 Pinyoanuntapong E, Ali A, Wang P, Lee M, Chen C (2022) GaitMixer: skeleton-based gait representation learning via wide-spectrum multi-axial mixer. arXiv preprint arXiv:​2210.​15491
9.
Zurück zum Zitat Vasilcová V (2022) Pilot study: effect of developmental dysplasia of the hip on the gait and feet posture. Stud Sport 16(2):134–142 Vasilcová V (2022) Pilot study: effect of developmental dysplasia of the hip on the gait and feet posture. Stud Sport 16(2):134–142
Metadaten
Titel
Identifying Human Movement Patterns: Multivariate Gait Analysis Through Machine Learning
verfasst von
Raunak Kumar
Usha Mittal
Priyanka Chawla
Copyright-Jahr
2025
Verlag
Springer Nature Singapore
DOI
https://doi.org/10.1007/978-981-97-1943-3_2