ORIGINAL RESEARCH article
Front. Sports Act. Living
Sec. Sports Science, Technology and Engineering
This article is part of the Research TopicHarnessing Artificial Intelligence in Sports Science: Enhancing Performance, Health, and EducationView all 12 articles
Unsupervised machine learning method to determine recovery profiles of elite canoe-kayak athletes during the preparation year for the Olympics Games
Provisionally accepted
- 1Espace Dev UPVD, Perpignan, France
- 2IRD, Espace Dev UMR 228, Montpellier, France
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Background: Continuous wearable monitoring generates high-volume data, yet methods to translate these streams into actionable recovery insights for elite athletes remain scarce. This study applied a multi-layer, unsupervised machine-learning pipeline to characterize nightly recovery states and season-long physiological phenotypes in Olympic-level French canoe-kayak paddlers. Methods: Seventeen national-team athletes (9 women) were followed for 5 855 nights (≈ 12 months). Internal load—heart rate, heart-rate variability (HRV), respiratory rate and 30 sleep-architecture variables—was captured with thoracic belts and validated smart rings; external load was logged via an online training platform. After data standardization and validation using multiple indices, K-means clustering was performed. Results: A four-cluster night typology (K0–K3) emerged (Silhouette = 0.52). Sleep quantity and fragmentation indices—time in bed, total sleep duration, light-sleep duration, efficiency, phase count and transitions—explained up to 79 % of between-cluster variance (η² ≥ 0.70). Nocturnal respiratory rate contributed an additional 15 %, whereas HR/HRV each accounted for ≤ 4 %. Forty-one percent of nights were classed as “optimized recovery” (K3), characterized by long, uninterrupted sleep and low respiratory rate. Athlete-level clustering yielded four profiles (A0–A3). Notably, the highest-performing cluster (A3) paradoxically combined slightly reduced sleep efficiency (85.9%) with superior cardiac-autonomic markers (HR: 46 bpm, HRV: 117 ms), suggesting that robust vagal tone may compensate for sub-optimal sleep quality—a finding that challenges conventional recovery paradigms Conclusion: Integrated sleep architecture is the dominant discriminator of nightly recovery state, while elevated respiratory rate flags residual metabolic strain. Stable season-long physiological signatures align closely with competitive success, underscoring the value of personalized, ML-driven recovery monitoring in high-performance sport. Athlete profile reveals that exceptional cardiac-autonomic tone can compensate for sub-optimal sleep efficiency in elite performers, suggesting that vagal dominance may be more critical than perfect sleep architecture for competitive success.
Keywords: High level athletes, Canoe-kayak, Recvery profiles, Olympics Games, Unsupervised machine learning (UML)
Received: 16 May 2025; Accepted: 27 Oct 2025.
Copyright: © 2025 Foucaud, Durand and Meric. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Fabienne Durand, fdurand@univ-perp.fr
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