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Pathological Gait Analysis With an Open-Source Cloud-Enabled Platform Empowered by Semi-Supervised Learning-PathoOpenGait.

Publication ,  Journal Article
Ho, M-Y; Kuo, M-C; Chen, C-S; Wu, R-M; Chuang, C-C; Shih, C-S; Tseng, YJ
Published in: IEEE journal of biomedical and health informatics
February 2024

We present PathoOpenGait, a cloud-based platform for comprehensive gait analysis. Gait assessment is crucial in neurodegenerative diseases such as Parkinson's and multiple system atrophy, yet current techniques are neither affordable nor efficient. PathoOpenGait utilizes 2D and 3D data from a binocular 3D camera for monitoring and analyzing gait parameters. Our algorithms, including a semi-supervised learning-boosted neural network model for turn time estimation and deterministic algorithms to estimate gait parameters, were rigorously validated on annotated gait records, demonstrating high precision and consistency. We further demonstrate PathoOpenGait's applicability in clinical settings by analyzing gait trials from Parkinson's patients and healthy controls. PathoOpenGait is the first open-source, cloud-based system for gait analysis, providing a user-friendly tool for continuous patient care and monitoring. It offers a cost-effective and accessible solution for both clinicians and patients, revolutionizing the field of gait assessment. PathoOpenGait is available at https://pathoopengait.cmdm.tw.

Duke Scholars

Published In

IEEE journal of biomedical and health informatics

DOI

EISSN

2168-2208

ISSN

2168-2194

Publication Date

February 2024

Volume

28

Issue

2

Start / End Page

1066 / 1077

Related Subject Headings

  • Supervised Machine Learning
  • Parkinson Disease
  • Humans
  • Gait Analysis
  • Gait
  • Algorithms
 

Citation

APA
Chicago
ICMJE
MLA
NLM
Ho, M.-Y., Kuo, M.-C., Chen, C.-S., Wu, R.-M., Chuang, C.-C., Shih, C.-S., & Tseng, Y. J. (2024). Pathological Gait Analysis With an Open-Source Cloud-Enabled Platform Empowered by Semi-Supervised Learning-PathoOpenGait. IEEE Journal of Biomedical and Health Informatics, 28(2), 1066–1077. https://doi.org/10.1109/jbhi.2023.3340716
Ho, Ming-Yang, Ming-Che Kuo, Ciao-Sin Chen, Ruey-Meei Wu, Ching-Chi Chuang, Chi-Sheng Shih, and Yufeng Jane Tseng. “Pathological Gait Analysis With an Open-Source Cloud-Enabled Platform Empowered by Semi-Supervised Learning-PathoOpenGait.IEEE Journal of Biomedical and Health Informatics 28, no. 2 (February 2024): 1066–77. https://doi.org/10.1109/jbhi.2023.3340716.
Ho M-Y, Kuo M-C, Chen C-S, Wu R-M, Chuang C-C, Shih C-S, et al. Pathological Gait Analysis With an Open-Source Cloud-Enabled Platform Empowered by Semi-Supervised Learning-PathoOpenGait. IEEE journal of biomedical and health informatics. 2024 Feb;28(2):1066–77.
Ho, Ming-Yang, et al. “Pathological Gait Analysis With an Open-Source Cloud-Enabled Platform Empowered by Semi-Supervised Learning-PathoOpenGait.IEEE Journal of Biomedical and Health Informatics, vol. 28, no. 2, Feb. 2024, pp. 1066–77. Epmc, doi:10.1109/jbhi.2023.3340716.
Ho M-Y, Kuo M-C, Chen C-S, Wu R-M, Chuang C-C, Shih C-S, Tseng YJ. Pathological Gait Analysis With an Open-Source Cloud-Enabled Platform Empowered by Semi-Supervised Learning-PathoOpenGait. IEEE journal of biomedical and health informatics. 2024 Feb;28(2):1066–1077.

Published In

IEEE journal of biomedical and health informatics

DOI

EISSN

2168-2208

ISSN

2168-2194

Publication Date

February 2024

Volume

28

Issue

2

Start / End Page

1066 / 1077

Related Subject Headings

  • Supervised Machine Learning
  • Parkinson Disease
  • Humans
  • Gait Analysis
  • Gait
  • Algorithms