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