Description
This dataset includes the input features and target labels needed to train and test FootNet. The input features include the distal tibia anteroposterior velocity, ankle plantar/dorsi flexion angle and foot centre of mass anteroposterior and vertical velocities. Additionally, ground reaction force data and trial names are also included.
| Date made available | 26 Jul 2021 |
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| Publisher | University of Bath |
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Clustering analysis across different speeds reveals two distinct running techniques with no differences in running economy
Rivadulla, A. R., Chen, X., Cazzola, D., Trewartha, G. & Preatoni, E., 31 Jan 2026, In: Sports Biomechanics. 25, 1, p. 22-45 24 p.Research output: Contribution to journal › Article › peer-review
Open AccessFile5 Link opens in a new tab Citations (SciVal)321 Downloads (Pure) -
Development and validation of FootNet; a new kinematic algorithm to improve foot-strike and toe-off detection in treadmill running
Rivadulla, A., Chen, X., Weir, G., Cazzola, D., Trewartha, G., Hamill, J. & Preatoni, E., 9 Aug 2021, In: PLoS ONE. 16, 8 August, e0248608.Research output: Contribution to journal › Review article › peer-review
Open Access16 Link opens in a new tab Citations (SciVal)
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