Predicting Temporal Gait Kinematics From Running Velocity

• Adrian J. Gray ; Michael Price ^[1] ; David Jenkins ^[2]
  1. [1] University of New England

     University of New England

     Australia

     
  2. [2] University of Queensland

     University of Queensland

     Australia

     
• Localización: Journal of strength and conditioning research: the research journal of the NSCA, ISSN 1064-8011, Vol. 35, Nº. 9, 2021, págs. 2379-2382
• Idioma: inglés
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• Resumen

  • The manner in which stride frequency (f) changes in response to running velocity (v) is well established. Notably, as running velocity increases, duty factor (d, the % of the stride in stance) decreases, concomitantly with higher stride frequencies. Mathematical descriptions of this relationship do not exist, limiting our ability to reasonably predict gait-based metrics from wearable technologies. Therefore, the purpose of this study was to establish prediction equations for stride frequency and duty factor from running velocity. On 2 occasions, 10 healthy men (aged, 21.1 +/- 2.2 years) performed constant pace running efforts at 3, 4, 5, 6, 7, and 8 m[middle dot]s-1 over a 10-m segment on a tartan athletics track. Running efforts were filmed using a digital video camera at 300 frames per second, from which stride duration, support duration, and swing duration were determined. Regression equations to predict stride frequency and duty factor from running velocity were established by curve fitting. Acceptable test-retest reliability for the video-based determination of stride frequency (intraclass correlation = 0.87; typical error of the measurement [TEM] = 0.01 Hz; coefficient of variation [CV] = 2.9%) and duty factor (r = 0.93; TEM = 1%; CV = 3.9%) were established. The relationship between stride frequency and running velocity was described by the following quadratic equation: f = 0.026[middle dot]v2 - 0.111[middle dot]v + 1.398 (r2 = 0.903). The relationship between duty factor and running velocity was described by the quadratic equation d = 0.004[middle dot]v2 - 0.061[middle dot]v + 0.50 (r2 = 0.652). The relationships between v and f and between v and d are consistent with previous observations. These equations may contribute broader locomotor models or serve as input variables in data fusion algorithms that enhance outputs from wearable technologies.