Department of Bioengineering, University of Pittsburgh, University of Pittsburgh, 3700 O'Hara St. #302, Pittsburgh, PA, 15261, USA.
Centre for Biomedical Engineering, Indian Institute of Technology (IIT) Delhi, Hauz Khas, New Delhi, 110016, India; Department of Biomedical Engineering, All India Institute of Medical Science (AIIMS), Ansari Nagar, New Delhi, 110029, India.
Appl Ergon. 2023 Jan;106:103854. doi: 10.1016/j.apergo.2022.103854. Epub 2022 Aug 13.
Measuring shoe-floor friction is critical for assessing the safety of footwear products. Portable devices for measuring coefficient of friction (COF) are needed. This study introduces such a device and evaluates its ability to predict human slip events across shoe designs. A portable device (18 kg) was utilized to measure 66 unique shoe-floor-fluid coefficients of friction (COF). Consistent with the shoes, flooring, and fluid contaminants from the COF tests, participants (n = 66) were unexpectedly exposed to the fluid while walking. Slip predictions were made based on a separate training data set. Slip predictions were made prospectively and using logistic regression analyses. The slip predictions were valid (p < 0.001), 91% sensitive, and 64% specific. The logistic regression fit also revealed that the COF values predicted slip outcomes (p = 0.006). This device is expected to expand the capacity of researchers, product developers, forensic engineers, and safety professionals to prevent slips and enhance human safety.
测量鞋底与地面之间的摩擦力对于评估鞋类产品的安全性至关重要。因此需要一种便携式的摩擦系数（COF）测量设备。本研究介绍了这样一种设备，并评估了其预测不同鞋类设计的人滑倒事件的能力。使用一个 18 公斤重的便携式设备来测量 66 种独特的鞋底-地面-液体摩擦系数（COF）。参与者（n=66）在行走时会意外地接触到与 COF 测试中相同的鞋子、地板和液体污染物。根据一个单独的训练数据集来进行滑倒预测。使用逻辑回归分析进行前瞻性和实时预测。滑倒预测是有效的（p<0.001），敏感性为 91%，特异性为 64%。逻辑回归拟合还表明 COF 值可以预测滑倒结果（p=0.006）。该设备有望扩大研究人员、产品开发人员、法医工程师和安全专业人员的能力，以防止滑倒并提高人类安全性。
