Personalized prediction of gait freezing using dynamic mode decomposition.

Zhiwen Fu, Congping Lin, Yiwei Zhang

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Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

Authors and funding

3 authors.

Zhiwen Fu *Department of Mathematics and SUSTech International Center for Mathematics, Southern University of Science and Technology, Shenzhen, China.

Congping Lin *School of Mathematics and Statistics, Hubei Key Lab of Engineering Modelling and Scientific, Center for Mathematical Sciences, Huazhong University of Science and Technology, Wuhan, China.

Yiwei ZhangDepartment of Mathematics and SUSTech International Center for Mathematics, Southern University of Science and Technology, Shenzhen, China. zhangyw@sustech.edu.cn.

Funding

Basic and Applied Basic Research Foundation of Guangdong Province 2024A1515010974Cultivation of Guangdong College Students' Scientific and Technological Innovation pdjh2024c20101National Natural Science Foundation of China 12161141002National Natural Science Foundation of China 12171179

6 · The paper itself

Abstract

Freezing of gait (FoG) is a common severe gait disorder in patients with advanced Parkinson's disease. The ability to predict the onset of FoG episodes early on allows for timely intervention, which is essential for improving the life quality of patients. Machine learning and deep learning, the current methods, face real-time diagnosis challenges due to comprehensive data processing requirements. Their "black box" nature makes interpreting features and classification boundaries difficult. In this manuscript, we explored a dynamic mode decomposition (DMD)-based approach together with optimal delay embedding time to reconstruct and predict the time evolution of acceleration signals, and introduced a triple index based on DMD to predict and classify FoG. Our predictive analysis shows 86.5% accuracy in classification, and an early prediction ratio of 81.97% with an average early prediction time of 6.13 s. This DMD-based approach has the potential for real-time patient-specific FoG prediction.

Indexed as

GaitGait Disorders, NeurologicParkinson DiseaseAgedAlgorithmsDeep LearningFemaleHumansMachine LearningMaleDynamic mode decompositionFreezing of gaitPersonalized predictionTriple index

Identifiers

PMID40437121

DOI10.1038/s41598-025-88110-4

PMCPMC12119878

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LicenceCC BY-NC-ND

Read underepoch 390

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.