时空步态特征对鞋子磨损率的影响研究

Sarah C. Griffin, Sarah L. Hemler, K. Beschorner
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摘要

我们调查了鞋子磨损率与描述个体时空步态特征的几个指标(节奏、步长和首选步行速度)之间的关系。没有发现任何关联,表明应该调查其他指标来预测工人磨损鞋底的个体化率。技术摘要背景:在工作场所,穿鞋与滑倒和跌倒的增加有关。人们磨损鞋面的速度不同;因此,个性化的换鞋时间表可以改善以时间作为换鞋基础的组织的资源目标。先前的研究发现,鞋子-地板动力学,如行走的摩擦要求,与鞋子的磨损率有关。使用容易测量的指标,如节奏、步长或首选步行速度来预测磨损尚未被研究,尽管它们与摩擦要求的关系。目的探讨鞋子磨损率与步态时空特征之间的关系。方法13名参与者完成了一项纵向鞋穿研究,包括步态评估,然后穿着两双防滑鞋长时间穿鞋。步态评估包括干平地步行试验;运动学和动力学数据通过光学运动捕捉和力板收集。计算参与者的平均步频、步长和首选步行速度。然后,参与者穿着鞋子上班;鞋子的磨损率是通过测量在工作中磨损部分的周期性胎面体积损耗来确定的。结果三种线性回归模型均未发现所选步态指标与鞋子磨损率之间存在显著相关性。结论时空步态特征与鞋子磨损率之间缺乏相关性,表明这些因素可能不能解释参与者之间磨损率的差异。这一消极的发现表明,其他措施,如所需的摩擦系数,更适合个性化的鞋类更换指南。
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Investigating the Influence of Spatiotemporal Gait Characteristics on Shoe Wear Rate
OCCUPATIONAL APPLICATIONS We investigated the association between shoe wear rate and several metrics describing an individual’s spatiotemporal gait characteristics (cadence, step length, and preferred walking speed). No associations were found, indicating that alternative metrics should be investigated to predict the individualized rate at which workers wear down shoe tread. TECHNICAL ABSTRACT Background Shoe wear has been associated with increased slips and falls in the workplace. People wear down shoe tread at different rates; therefore, individualized shoe replacement timelines could improve resource targeting for organizations that use time as a basis for shoe replacement. Previous work has found that the shoe-floor kinetics, such as the friction requirements of walking, correlate with shoe wear rate. The use of easily measured metrics such as cadence, step length, or preferred walking speed to predict wear has not yet been investigated despite their relationship with friction requirements. Purpose This study seeks to determine the association between shoe wear rate and gait spatiotemporal characteristics. Methods Thirteen participants completed a longitudinal shoe wear study that consisted of a gait assessment followed by prolonged shoe wear in two pairs of slip-resistant shoes. The gait assessment was comprised of dry level-ground walking trials; kinematic and kinetic data were collected through optical motion capture and force plates. The participants’ mean cadence, step length, and preferred walking speed were calculated. The participants then wore their shoes at work; the shoe wear rate was determined by measuring the periodic volumetric tread loss during this wear-at-work portion of the study. Results Three linear regression models found no significant association between the chosen gait metrics and the shoe wear rate. Conclusions The lack of an association between the spatiotemporal gait characteristics and shoe wear rate indicates that these factors may not explain the differences in wear rate between participants. This negative finding suggests that other measures such as the required coefficient of friction are better for individualizing footwear replacement guidelines.
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