The use of transfer learning to take estimation of running ground reaction forces via wearables from the treadmill to overground
Year: 2025
Type: conference-paper
Abstract: This study found that a transfer learning approach using ground reaction force (GRF) data from treadmill running helped improve GRF estimation accuracy of a deep learning model during overground running. Leveraging a treadmill dataset 70 times larger than the overground dataset enabled the model to learn generalisable features that could be adapted to the target use case, resulting in more accurate overground GRF predictions than training on the smaller dataset alone.
Source: Pure (University of Bath)
Authors: Carter, Joshua, Chen, Xi; id_orcid 0000-0002-3577-3308, Cazzola, Dario; id_orcid 0000-0001-7877-6755, Trewartha, Grant, Preatoni, Ezio; id_orcid 0000-0001-5383-7072
Institution: Teesside University
Language: English
Subfield: Biomedical Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7115321811
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