Predicting Vertical Ground Reaction Forces in Running from the Sound of Footsteps

Abstract

From the point of view of measurement, footstep sounds represent a simple, wearable and inexpensive sensing opportunity to assess running biomechanical parameters. Therefore, the aim of this study was to investigate whether the sounds of footsteps can be used to predict the vertical ground reaction force profiles during running. Thirty-seven recreational runners performed overground running, and their sounds of footsteps were recorded from four microphones, while the vertical ground reaction force was recorded using a force plate. We generated nine different combinations of microphone data, ranging from individual recordings up to all four microphones combined. We trained machine learning models using these microphone combinations and predicted the ground reaction force profiles by a leave-one-out approach on the subject level. There were no significant differences in the prediction accuracy between the different microphone combinations (p < 0.05). Moreover, the machine learning model was able to predict the ground reaction force profiles with a mean Pearson correlation coefficient of 0.99 (range 0.79–0.999), mean relative root-mean-square error of 9.96% (range 2–23%) and mean accuracy to define rearfoot or forefoot strike of 77%. Our results demonstrate the feasibility of using the sounds of footsteps in combination with machine learning algorithms based on Fourier transforms to predict the ground reaction force curves. The results are encouraging in terms of the opportunity to create wearable technology to assess the ground reaction force profiles for runners in the interests of injury prevention and performance optimization.

Impact of PCA Pre-Normalization Methods on Ground Reaction Force Estimation Accuracy

“…The authors in [ 3 ] used the min–max method to estimate position in indoor navigation. The authors in [ 4 ] estimated the vertical component of the ground reaction force (GRF) from step sound using body weight normalization. Honert et al [ 5 ] estimated the vertical and anterior–posterior components of the GRF using the Z−score (ZS) normalization method.…”

“…Statistical methods encompass standard techniques, like the min–max and ZS methods. Physics methods rely on the parameters specific to the domain of the database [ 4 , 12 , 13 , 14 ]. This is the reason why studying the impact of normalization methods in ML estimation accuracy must be carried out in a specific domain.…”

Prediction of instantaneous perceived effort during outdoor running using accelerometry and machine learning

“…Machine learning is being increasingly used in sports, as sports performance is relying more on objective data within the last decades. There are several studies applying machine learning in running to predict impact loading/ground reaction forces (Girka et al 2020 ; Oliveira et al 2022 ), running velocity (Wiecha et al 2022 ), and terrain types a runner is exposed to (Dixon et al 2019 ). Moreover, fatigue during running has been investigated using metabolic (Bustos et al 2022 ) and mechanical variables (Gholami et al 2020 ; Marotta et al 2021 ).…”