Estimating running kinematics variability with an IMU sensor placed on the runner's thorax

Our contribution consists in a deep learning algorithm able to estimate running variability from a single IMU sensor placed on the runner's thorax. The algorithm has been trained and tested with data collected in indoor conditions, at different locomotion speeds and uphill/downhill grades. The reference values for running variability were provided by an opto-electronic system. Results suggest that running kinematic variables (stride frequency and duty factor) and variability (DFA-a and Higuchi's D) could be estimated by the algorithm with an accuracy compatible to that obtained with a couple of inertial measurement units (IMU) placed at the foot level. Limitations apply, but it is suggested that the algorithm developed here could constitute a unique tool to assess running kinematic variables and variability indices with a single IMU placed at the thorax level.