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.
Original languageEnglish
Title of host publicationISBS Proceedings Archives
Subtitle of host publication41st International Conference on Biomechanics in Sports (2023) Milwaukee, USA, July 12-16, 2023
PublisherInternational Society of Biomechanics in Sports (ISBS)
Number of pages4
Publication statusPublished - 16 Jul 2023
EventInternational Conference on Biomechanics in Sports - Marquette University, Milwaukee, USA United States
Duration: 12 Jul 202316 Jul 2023
Conference number: 41st


ConferenceInternational Conference on Biomechanics in Sports
Abbreviated titleISBS 2023
Country/TerritoryUSA United States
Internet address

Bibliographical note

ISBS Proceedings Archive: Vol. 41 : Iss. 1. 41st International Conference on Biomechanics in Sports (2023) Milwaukee, USA, July 12-16, 2023


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