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Li, S, Qu, R, Zhang, Y and Yu, D (2023) Research on Freezing of Gait Recognition Method Based on Variational Mode Decomposition. Intelligent Automation & Soft Computing, 37 (3). pp. 2809-2823. ISSN 1079-8587
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Research on Freezing of Gait Recognition Method Based on Variational Mode Decomposition.pdf - Published Version Available under License Creative Commons Attribution. Download (1MB) | Preview |
Abstract
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 hyperparam-eters 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%.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | 0801 Artificial Intelligence and Image Processing; 1702 Cognitive Sciences; Artificial Intelligence & Image Processing |
| Subjects: | T Technology > TA Engineering (General). Civil engineering (General) |
| Divisions: | Engineering |
| Publisher: | Tech Science Press |
| SWORD Depositor: | A Symplectic |
| Date Deposited: | 09 Aug 2024 15:01 |
| Last Modified: | 09 Aug 2024 15:01 |
| DOI or ID number: | 10.32604/iasc.2023.036999 |
| URI: | https://researchonline.ljmu.ac.uk/id/eprint/23915 |
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