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Research on Freezing of Gait Recognition Method Based on Variational Mode Decomposition

Shoutao Li1,2,*, Ruyi Qu1, Yu Zhang1, Dingli Yu3

1 School of Communication Engineering, Jilin University, Changchun, 130012, China
2 Academy of Electric Information, Changchun University of Architecture and Civil Engineering, Changchun, 130607, China
3 School of Engineering and Technology, Liverpool John Moores University, Liverpool, L33AF, UK

* Corresponding Author: Shoutao Li. Email:

Intelligent Automation & Soft Computing 2023, 37(3), 2809-2823.


Freezing of Gait (FOG) is the most common and disabling gait disorder in patients with Parkinson’s Disease (PD), which seriously affects the life quality and social function of patients. This paper proposes a FOG recognition method based on the Variational Mode Decomposition (VMD). Firstly, VMD instead of the traditional time-frequency analysis method to complete adaptive decomposition to the FOG signal. Secondly, to improve the accuracy and speed of the recognition algorithm, use the CART model as the base classifier and perform the feature dimension reduction. Then use the RUSBoost ensemble algorithm to solve the problem of unbalanced sample size and considerable limitations of a single classifier. Finally, the hyperparameters of the ensemble classifier are optimized by Bayesian optimization, and the experiment proves that the RUSBoost algorithm can complete the gait recognition task well. Compared with the Adaboost, Tomeklinks-Adaboost and ROS-Adaboost ensemble algorithms, the RUSBoost ensemble algorithm can complete the FOG recognition task more efficiently. When the maximum number of splits is 1023, and the number of base classifiers is 100, the performance of the RUSBoost ensemble algorithm can reach the best. The accuracy of the time recognition algorithm was 87.8%, the sensitivity was 89.7%, and the specificity was 87.5%.


Cite This Article

S. Li, R. Qu, Y. Zhang and D. Yu, "Research on freezing of gait recognition method based on variational mode decomposition," Intelligent Automation & Soft Computing, vol. 37, no.3, pp. 2809–2823, 2023.

This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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