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

Formative Evaluation of Digital Health Interventions (5370) New Methods (1869) mHealth for Data Collection and Research (1779) mHealth for Telemedicine and Homecare (900) Imaging Informatics (506) Machine Learning (3333)

Published on in Vol 10 (2026)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/88488, first published .
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Autoencoder-Enhanced Convolutional Neural Networks for Plantar Pressure–Based Gait Pattern Recognition: Model Development and Cross-Validated Evaluation Study

Autoencoder-Enhanced Convolutional Neural Networks for Plantar Pressure–Based Gait Pattern Recognition: Model Development and Cross-Validated Evaluation Study

Authors of this article:

Chuan-Chun Chang1 Author Orcid Image ;   Chi-Wen Lung2, 3 Author Orcid Image ;   Yih-Kuen Jan3 Author Orcid Image ;   Qi-Qian Lu1 Author Orcid Image ;   Yi-You Wang1 Author Orcid Image ;   Yi-Sheng Chen4 Author Orcid Image ;   Ben-Yi Liau1 Author Orcid Image
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Journals

  1. Dondapati N, Vasimalla Y, Mogal M, Kumar S. Machine learning–enhanced optical biosensors: Advanced modelling, signal intelligence, and next-generation surface plasmon resonance platforms. Next Materials 2026;13:103098 View