Estimation of horizontal running power using foot-worn inertial measurement units - Apte, Salil; Falbriard, Mathieu; Meyer, Frédéric; Millet, Grégoire P.; Gremeaux, Vincent; Aminian, Kamiar (Frontiers in Bioengineering and Biotechnology, 2023)
Feedback of power during running is a promising tool for training and determining pacing strategies. However, current power estimation methods show low validity and are not customized for running on different slopes. To address this, we developed three machine-learning models to estimate peak horizontal power for level, uphill, and downhill running using gait spatiotemporal parameters, accelerometer and gyroscope signals extracted from foot-worn IMU. The prediction was compared to reference horizontal power obtained during running on treadmill with an embedded force plate. For each model, we trained an elastic net and a neural network and validated it with a dataset of 34 active adults across a range of speeds and slopes. For the uphill and level running, the concentric phase of gait cycle was considered, and the neural network model led to the lowest error (median ± interquartile range) of 1.7 ± 12.5 % and 3.2 ± 13.4 %, respectively. The eccentric phase was considered relevant for downhill running, wherein, the elastic net model provided the lowest error of 1.8 ± 14.1%. Results demonstrated a similar performance across a range of various speed/slope running conditions. The findings highlighted the potential of using interpretable biomechanical features in machine learning models for estimation of horizontal power. The simplicity of the models makes them suitable for implementation on embedded systems with limited processing and energy storage capacity. The proposed method meets requirements for applications needing accurate near real-time feedback and complements existing gait analysis algorithms based on foot worn IMUs.