Machine Learning and Data Analytics Lab, Department Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), 91052 Erlangen, Germany.
adidas AG, 91074 Herzogenaurach, Germany.
Sensors (Basel). 2024 Jul 19;24(14):4694. doi: 10.3390/s24144694.
Advanced footwear technology featuring stack heights higher than 30 mm has been proven to improve running economy in elite and recreational runners. While it is understood that the physiological benefit is highly individual, the individual biomechanical response to different stack heights remains unclear. Thirty-one runners performed running trials with three different shoe conditions of 25 mm, 35 mm, and 45 mm stack height on an outdoor running course wearing a STRYD sensor. The STRYD running variables for each participant were normalized to the 25 mm shoe condition and used to cluster participants into three distinct groups. Each cluster showed unique running patterns, with leg spring stiffness and vertical oscillation contributing most to the variance. No significant differences were found between clusters in terms of body height, body weight, leg length, and running speed. This study indicates that runners change running patterns individually when running with footwear featuring different stack heights. Clustering these patterns can help understand subgroups of runners and potentially support running shoe recommendations.
高级鞋类技术的鞋跟高度超过 30 毫米已被证明可以提高精英和休闲跑步者的跑步经济性。虽然人们知道生理益处因人而异,但不同鞋跟高度对个体生物力学的影响仍不清楚。31 名跑步者在户外跑步道上穿着 STRYD 传感器,分别进行了 25 毫米、35 毫米和 45 毫米鞋跟高度的三种不同鞋类条件的跑步测试。每位参与者的 STRYD 跑步变量都被归一化为 25 毫米鞋类条件,并用于将参与者聚类为三个不同的组。每个聚类都表现出独特的跑步模式,腿部弹簧刚度和垂直振荡对变异性的贡献最大。在聚类中,身体高度、体重、腿长和跑步速度之间没有发现显著差异。本研究表明,当跑步者穿着具有不同鞋跟高度的鞋子跑步时,他们会单独改变跑步模式。对这些模式进行聚类可以帮助了解跑步者的亚组,并可能支持跑鞋推荐。