Human slip perception while walking on ice: Can we rely on self-reported slip counts for winter footwear evaluations?

IF 3.1 2区 工程技术 Q2 ENGINEERING, INDUSTRIAL
Davood Dadkhah , Hamed Ghomashchi , Tilak Dutta
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引用次数: 0

Abstract

Fall-related injuries on icy surfaces are a major public health concern. Slip-resistant winter boots that incorporate the latest composite outsole technologies have demonstrated the potential to prevent falls in winter weather in lab-based testing. However, the real-world benefits of this composite footwear remain difficult to measure because of a lack of accurate evaluation methods. In particular, existing methods rely on comparing self-reported slip counts to identify differences in slip resistance performance between different footwear models. However, prior research has primarily focused on slip detection on soapy and oily surfaces, revealing that small slips (30 mm) often go undetected, with humans correctly identifying them only 50% of the time. No studies have yet examined slip perception on icy surfaces, which possess significantly lower coefficients of friction compared to soapy and oily environments. The objective of this study was to investigate the agreement between self-reported slip counts and motion capture detected slips while walking on ice with winter footwear.
Twenty-five healthy participants were asked to walk on ice surfaces (melting ice 0.5 ± 1.0 °C and cold ice -3.5 ± 1.0 °C) while wearing three models of winter boots with varying slip resistance performance (poor, moderate, good) and were asked to report any slips they experienced. Ground truth slip identification and slip length measurement was done using an 8-camera Vicon motion capture system. Slips were categorized as small slips (30 mm), moderate slips (30–100 mm), or large slips (>100 mm) for each boot and the proportion detected by participants was calculated.
A total of 7743 slips were identified from 53,944 steps captured by the motion capture system with 4395, 1999 and 1349 slips recorded from the boots with poor, moderate and good slip resistance, respectively. These included 1658 small slips, 2521 moderate slips, and 3564 large slips. Overall, participants only reported 38.3% of these slips including 375 small slips (22.6% reported), 823 moderate slips (32.6% reported) and 1767 large slips (49.6% reported). These findings showed a strong positive correlation between self-reported slips and slip length (ρ = 0.573, p<0.001) demonstrating that participants were significantly more likely to report larger slips.
The findings of this study demonstrate the need to develop more objective methods of recording slip events for real-world winter footwear evaluations.
人类在冰上行走时的滑动感知:我们能依靠自我报告的滑动计数来评估冬季鞋类吗?
在结冰的地面上摔倒造成的伤害是一个主要的公共卫生问题。防滑冬季靴采用了最新的复合大底技术,在实验室测试中证明了在冬季防止跌倒的潜力。然而,由于缺乏准确的评估方法,这种复合鞋的实际效益仍然难以衡量。特别是,现有的方法依赖于比较自我报告的打滑次数,以确定不同鞋类模型之间防滑性能的差异。然而,之前的研究主要集中在肥皂和油性表面的滑动检测上,揭示了小滑动(≤30毫米)通常无法被检测到,人类正确识别它们的概率只有50%。目前还没有研究检测冰面上的滑动感知,与肥皂和油性环境相比,冰面的摩擦系数要低得多。本研究的目的是调查自我报告的滑倒次数和运动捕捉检测到的滑倒在冰上行走时的一致性。25名健康的参与者被要求在冰面上行走(融冰0.5±1.0°C和冷冰-3.5±1.0°C),同时穿着三种不同防滑性能的冬季靴子(差、中等、良好),并被要求报告他们所经历的任何滑倒。地面真滑识别和滑长测量使用8摄像头Vicon运动捕捉系统。每个靴子的滑动被分类为小滑动(≤30 mm),中等滑动(30 - 100 mm)或大滑动(>100 mm),并计算参与者检测到的比例。从运动捕捉系统捕获的53,944个步骤中,共识别出7743个滑动,其中从抗滑性差、中等和良好的靴子中分别记录了4395、1999和1349个滑动。其中包括1658个小卡瓦,2521个中等卡瓦和3564个大卡瓦。总体而言,参与者仅报告了38.3%的滑移,包括375个小滑移(22.6%报道),823个中度滑移(32.6%报道)和1767个大滑移(49.6%报道)。这些发现表明,自我报告的滑倒和滑倒长度之间存在很强的正相关(ρ = 0.573, p<0.001),表明参与者更有可能报告较大的滑倒。本研究的结果表明,需要开发更客观的方法来记录滑倒事件,以用于实际的冬季鞋类评估。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Applied Ergonomics
Applied Ergonomics 工程技术-工程:工业
CiteScore
7.50
自引率
9.40%
发文量
248
审稿时长
53 days
期刊介绍: Applied Ergonomics is aimed at ergonomists and all those interested in applying ergonomics/human factors in the design, planning and management of technical and social systems at work or leisure. Readership is truly international with subscribers in over 50 countries. Professionals for whom Applied Ergonomics is of interest include: ergonomists, designers, industrial engineers, health and safety specialists, systems engineers, design engineers, organizational psychologists, occupational health specialists and human-computer interaction specialists.
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