作者：
Kaylie Lau*;Takeshi Yamaguchi;Kei Shibata;Toshiaki Nishi;Geoff Fernie;...
通讯作者：
Kaylie Lau
作者机构：
Toronto Rehabilitation Institute, University Health Network, Toronto, Canada
University of Toronto, Institute of Biomaterials and Biomedical Engineering, Toronto, Canada
Tohoku University, Department of Finemechanics, Sendai, Miyagi, Japan
Tohoku University, Graduate School of Biomedical Engineering, Sendai, Miyagi, Japan
National Institute of Occupational Safety and Health, Japan, Kiyose, Tokyo, Japan
通讯机构：
Toronto Rehabilitation Institute, University Health Network, Toronto, Canada
University of Toronto, Institute of Biomaterials and Biomedical Engineering, Toronto, Canada
语种：
英文
关键词：
Coefficient of friction,Shoe safety,Shoe friction assessment,Slip resistant,Machine learning
期刊：
Applied Ergonomics
ISSN：
0003-6870
年：
2024
卷：
117
页码：
104249
基金类别：
Canadian Institutes of Health Research (CIHR) Foundation [FDN-148450
摘要：
Slippery surfaces due to oil spills pose a significant risk in various environments, including industrial workplaces, kitchens, garages, and outdoor areas. These situations can lead to accidents and falls, resulting in injuries that range from minor bruises to severe fractures or head trauma. To mitigate such risks, the use of slip resistant footwear plays a crucial role. In this study, we aimed to develop an Artificial Intelligence model capable of classifying footwear as having either high or low slip resistance based on the geometric characteristics and material parameters of their outsoles. Our model was trained on a unique dataset comprising images of 37 indoor work ...
