ESTIMATION OF GROUND REACTION FORCE DURING RUNNING USING CONSUMER-LEVEL WEARABLE INSOLES AND MACHINE LEARNING

Authors

Josh Carter, University of Bath, UKFollow
Xi Chen, University of Bath, UKFollow
Dario Cazzola, University of Bath, UKFollow
Grant Trewartha, NURVV Ltd., UKFollow
Ezio Preatoni, University of Bath, UKFollow

Category

Wearable Technology

Document Type

Paper

Abstract

Data from NURVV Run, a consumer-level wearable technology product, embedding pressure insoles and inertial transducers, were used as an input into a deep learning model for the estimation of vertical ground reaction forces (vGRF) during running. Force data were collected from an instrumented treadmill during a running protocol of mixed gradients and speeds, serving as the gold standard to evaluate the model accuracy. Mean difference in peak vGRF was 0.36 ± 0.26 BW across participants and mean root mean squared error was 0.27 ± 0.15 BW. Model accuracy varied considerably between participants; it would be expected that a larger dataset with a greater variety of input variables would improve on this. A future version of this model could allow continual assessment of load accumulation during distance running, helping identify early signs of elevated injury risk.

Recommended Citation

Carter, Josh; Chen, Xi; Cazzola, Dario; Trewartha, Grant; and Preatoni, Ezio (2023) "ESTIMATION OF GROUND REACTION FORCE DURING RUNNING USING CONSUMER-LEVEL WEARABLE INSOLES AND MACHINE LEARNING," ISBS Proceedings Archive: Vol. 41: Iss. 1, Article 19.
Available at: https://commons.nmu.edu/isbs/vol41/iss1/19