A novel method to predict slip resistance of winter footwear using a convolutional neural network
Author:
Affiliation:
1. KITE - Toronto Rehabilitation Institute, University Health Network, Toronto, Canada
2. Institute of Biomedical Engineering, University of Toronto, Toronto, Canada
3. Department of Surgery, University of Toronto, Toronto, Canada
Funder
Rehabilitation Engineering Research Centre
Publisher
Informa UK Limited
Subject
Biomedical Engineering,Physical Therapy, Sports Therapy and Rehabilitation,Orthopedics and Sports Medicine,Biophysics,Human Factors and Ergonomics
Link
https://www.tandfonline.com/doi/pdf/10.1080/19424280.2023.2198987
Reference38 articles.
1. 2010 ADA Standards for Accessible Design. (2010). https://www.ada.gov/law-and-regs/design-standards/2010-stds/
2. Reducing fall risk for home care workers with slip resistant winter footwear
3. Reducing the Risk of Falls by 78% with a New Generation of Slip Resistant Winter Footwear
4. Slip resistance and wearability of safety footwear used on icy surfaces for outdoor municipal workers
5. Modeling Mixed-Lubrication of a Shoe-Floor Interface Applied to a Pin-on-Disk Apparatus
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1. Slip-resistance of rubbers and polymer fibers as shoe sole on dry and wet ice surfaces;Tribology International;2024-10
2. Machine learning prediction of footwear slip resistance on glycerol-contaminated surfaces: A pilot study;Applied Ergonomics;2024-05
3. Slip-Resistance of Rubbers and Polymer Fibers as Shoe Sole on Dry and Wet Ice Surfaces;2024
4. Development of a novel biomedical device for shoe traction safety characterization;Journal of Engineering Research;2023-08
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