利用可穿戴传感器在跑步过程中对足触地评估进行验证。
Validation of Foot-Strike Assessment Using Wearable Sensors During Running.
机构信息
Department of Kinesiology, University of Virginia, Charlottesville.
出版信息
J Athl Train. 2020 Dec 1;55(12):1307-1310. doi: 10.4085/1062-6050-0520.19.
Wearable sensors are capable of capturing foot-strike positioning, which lends insight into landing biomechanics during running. The purpose of our study was to assess the relationship between foot-strike categorization and foot-strike angle during running to validate the sensor-derived foot-strike outcome. Twenty collegiate cross-country athletes (12 females, 8 males) ran at 2 speeds on an instrumented treadmill. RunScribe sensors were used to determine foot-strike categorizations (1-5 = rearfoot, 6-10 = midfoot, 11-16 = forefoot), and foot-strike angles were simultaneously assessed with 3-dimensional motion capture bilaterally. We calculated Pearson r correlation coefficients to compare foot-strike categorizations and angles at initial contact over 800 steps as well as sensor foot-strike identification accuracy. A strong, inverse correlation between foot-strike categorizations and foot-strike angles was present (r = -0.86, P < .001). Overall, the sensors demonstrated 78% accuracy (rearfoot = 72.5%, midfoot = 55.3%, forefoot = 95.4%). These results support the concurrent validity of the sensor-derived foot-strike measures.
可穿戴传感器能够捕捉到足触位置，从而深入了解跑步时的着陆生物力学。我们的研究目的是评估跑步时的足触分类和足触角度之间的关系，以验证传感器得出的足触结果。20 名大学生越野运动员（12 名女性，8 名男性）在带仪器的跑步机上以 2 种速度跑步。RunScribe 传感器用于确定足触分类（1-5=后跟，6-10=中足，11-16=前足），同时使用 3 维运动捕捉双侧评估足触角度。我们计算了 Pearson r 相关系数，以比较 800 步内初始接触时的足触分类和角度，以及传感器足触识别的准确性。足触分类和足触角度之间存在很强的负相关（r = -0.86，P <.001）。总体而言，传感器的准确率为 78%（后跟=72.5%，中足=55.3%，前足=95.4%）。这些结果支持了传感器得出的足触测量的同时效度。
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