This paper is in the following e-collection/theme issue:

Artificial Intelligence (1752) Clinical Informatics (1167) Clinical Information and Decision Making (1664) Decision Support for Health Professionals (1345) Machine Learning (1747) AI in Neurotechnology (6)

Published on in Vol 25 (2023)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/46427, first published .
Deep Learning–Assisted Gait Parameter Assessment for Neurodegenerative Diseases: Model Development and Validation

Deep Learning–Assisted Gait Parameter Assessment for Neurodegenerative Diseases: Model Development and Validation

Deep Learning–Assisted Gait Parameter Assessment for Neurodegenerative Diseases: Model Development and Validation

Authors of this article:

Yu Jing1, 2 Author Orcid Image ;   Peinuan Qin3 Author Orcid Image ;   Xiangmin Fan1, 4 Author Orcid Image ;   Wei Qiang1, 2 Author Orcid Image ;   Zhu Wencheng4 Author Orcid Image ;   Wei Sun1 Author Orcid Image ;   Feng Tian1 Author Orcid Image ;   Dakuo Wang5 Author Orcid Image
Article Authors Cited by (5) Tweetations (3) Metrics

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  2. Mazurek K, Barnard L, Botha H, Christianson T, Graff-Radford J, Petersen R, Vemuri P, Windham B, Jones D, Ali F. A validation study demonstrating portable motion capture cameras accurately characterize gait metrics when compared to a pressure-sensitive walkway. Scientific Reports 2024;14(1) View
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  4. Yin W, Zhu W, Gao H, Niu X, Shen C, Fan X, Wang C. Gait analysis in the early stage of Parkinson’s disease with a machine learning approach. Frontiers in Neurology 2024;15 View
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