Abstract

Accurate calculation of temporal stride parameters is essential in horse gait analysis. A prerequisite for calculating these parameters is identifying the exact timings of gait events, i.e., hoof-on and hoof-off moments. A hoof-mounted inertial measurement unit (IMU) can be used to identify these moments accurately, yet this approach is often impractical due to the vulnerability of IMU to the impacts during locomotion. In this study, we investigated the possibility of accurately estimating the gait events using the signals of an IMU mounted on a less vulnerable location, such as a limb or upper body. To achieve the goal, we equipped IMUs on horses limbs, withers, and sacrum and measured them during different gaits. Then, we estimated the gait events timings by training recurrent neural networks models on the output signals of each IMU. Finally, we evaluated the models by comparing their results to the gait events timings labeled from hoof-mounted IMUs. The best performing model represented the best location (between the limbs, withers, and sacrum) for gait event estimation. Compared to the previous studies, our models yielded higher accuracy and were more generic by supporting more gaits. In conclusion, accurate calculation of temporal stride parameters is feasible by estimating gait event timings using an IMU mounted on less vulnerable body locations.

Full Text
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