Estimating the peak vertical ground reaction force component and step time in treadmill running using machine learning - a pilot study
This study aims to investigate the efficacy of a stacking approach to estimate parameters in treadmill running. Nineteen participants ran on a treadmill at self-selected pace. Ground reaction force and kinematic data were collected. Stacking in machine learning was used to estimate the peak vertical ground reaction force and step time. Good agreement was observed in the test data set for predicted and measured values of the peak vertical ground reaction force component and step time where the ICC values were 0.85 and 0.99 respectively. This suggests stacking may be a feasible approach to estimate kinetic and kinematic parameters during treadmill running.
© Copyright 2020 ISBS Proceedings Archive (Michigan). Northern Michigan University. Published by International Society of Biomechanics in Sports. All rights reserved.
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| Notations: | training science technical and natural sciences endurance sports |
| Tagging: | künstliche Intelligenz deep learning Algorithmus maschinelles Lernen Laufband Schrittanalyse |
| Published in: | ISBS Proceedings Archive (Michigan) |
| Language: | English |
| Published: |
Liverpool
International Society of Biomechanics in Sports
2020
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| Online Access: | https://commons.nmu.edu/isbs/vol38/iss1/90 |
| Volume: | 38 |
| Issue: | 1 |
| Pages: | Article 90 |
| Document types: | congress proceedings |
| Level: | advanced |