Quantifying training response in cycling based on cardiovascular drift using machine learning
Cardiovascular drift and aerobic decoupling are reliable indicators of response to training stimulus. ML is a promising tool for monitoring training response in endurance sports, offering early and sensitive insights into fitness adaptations or fatigue that can support more personalized training decisions for coaches and athletes.
Authors
- Artur Barsumyan (ORCID: https://orcid.org/0009-0007-1929-5581)
- René Burchard (ORCID: https://orcid.org/0000-0001-6113-3577)
- Jan A. Graw (ORCID: https://orcid.org/0000-0002-6920-8868)
- Christian Soost
- Raman Shyla
- Anton Saukkonen
Institutions
- Philipps University of Marburg (DE)
- University of Siegen (DE)
- Universitätsklinikum Gießen und Marburg (DE)
- Dilla University (ET)
- University Hospital Ulm (DE)
- Aalto University (FI)
Publication Details
- Journal
- Frontiers in Artificial Intelligence
- Published
- 2025-07-04
- DOI
- https://doi.org/10.3389/frai.2025.1623384
- Citations
- 1
- Primary Topic
- Cardiovascular and exercise physiology
- Type
- article
- Field-Weighted Citation Impact
- 2.38
Funders
- Philipps-Universität Marburg