Abstract
Accelerometers and gyroscopes are used to detect foot strike (FS), i.e., the moment when the foot first touches the ground. However, it is unclear whether different conditions (footwear hardness or foot strike pattern) influence the accuracy and precision of different FS detection methods when using such micro-electromechanical sensors (MEMS). This study compared the accuracy of four published MEMS-based FS detection methods with each other and the gold standard (force plate) to establish the most accurate method with regard to different foot strike patterns and footwear conditions. Twenty-three recreational runners (12 rearfoot and 11 forefoot strikers) ran on a 15-m indoor track at their individual running speed in three footwear conditions (low to high hardness). MEMS and a force plate were sampled at a rate of 3750 Hz. Individual accuracy and precision of FS detection methods were found which were dependent on running styles and footwear conditions. Most of the methods were characterized by a delay which generally increased from rearfoot to forefoot strike pattern and from high to low midsole hardness. It can be concluded that only one of the four methods can accurately determine FS in a variety of conditions.
Highlights
Biomechanical investigations of running require accurate detection methods of foot strike (FS), i.e., the exact moment when the foot touches the ground
A synchronized force plate was used as the reference
The lowest rearfoot stiffness was found for PUMA Speed 1000 (PU1000) (143.3 ± 0.1 N/mm) and the highest was found for PUMA Speed 100 (PU100) (302.9 ± 0.1 N/mm) (Table 1)
Summary
Biomechanical investigations of running require accurate detection methods of foot strike (FS), i.e., the exact moment when the foot touches the ground. This is of great importance to determine the FS angle in continuous kinematic data stream signals of foot orientation in the sagittal plane. This was performed by Heidenfelder et al [1], Hein and Grau [2], and Hollander et al [3] when investigating the FS angle under various conditions (footwear conditions or barefoot vs shod) in the laboratory. Exact FS detection enhances data accuracy and minimizes defective variability in running parameters, e.g., stride length and stride frequency, which have been investigated by various authors [3,5,6,7,8]
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