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Formative Evaluation of Digital Health Interventions (3499) New Methods (1191) mHealth for Data Collection and Research (1274) mHealth for Telemedicine and Homecare (569) Imaging Informatics (340) Machine Learning (2260)

Published on in Vol 10 (2026)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/88488, first published .
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

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
Article Authors Cited by Tweetations Metrics

Chuan-Chun Chang   1 , MS ;   Chi-Wen Lung   2, 3 , PhD ;   Yih-Kuen Jan   3 , PhD ;   Qi-Qian Lu   1 , MS ;   Yi-You Wang   1 , MS ;   Yi-Sheng Chen   4 , PhD ;   Ben-Yi Liau   1 , PhD

1 Department of Automatic Control Engineering, Feng Chia University, Taichung, Taiwan

2 Department of Creative Product Design, Asia University, Taichung, Taiwan

3 Rehabilitation Engineering Lab, Department of Health and Kinesiology, University of Illinois Urbana-Champaign, Urbana, IL, United States

4 Department of Communications Engineering, Feng Chia University, Taichung, Taiwan

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