Henri Méric
Biographic Data
| ID | 6890985 |
|---|---|
| NAME | Henri Méric |
| GIVEN NAMES | Henri |
| FAMILY NAME | Méric |
| SIGNATURE | MERIC H |
| AFFILIATIONS | Université de Perpignan |
| ORCID | 0000-0002-4336-2461 |
| VERIFIED | Yes |
| TOTAL WORKS | 2 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 2 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2024 |
| LATEST PUBLICATION YEAR | 2025 |
| H-INDEX | 0 |
Using unsupervised machine learning to characterize recovery patterns in elite canoe-kayak athletes across the Olympic training year
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 5,85…
The role of machine learning methods in physiological explorations of endurance trained athletes
Endurance-trained athletes require physiological explorations that have evolved throughout the history of exercise physiology with technological advances. From the use of the Douglas bag to measure gas exchange to the development of wearable connected devices, advances in physiological explorations have enabled us to move from the classic but still widely used cardiopulmonary exercise test (CPET) to the collection of data under real conditions on…
No prominent works on this page.
The role of machine learning methods in physiological explorations of endurance trained athletes
Endurance-trained athletes require physiological explorations that have evolved throughout the history of exercise physiology with technological advances. From the use of the Douglas bag to measure gas exchange to the development of wearable connected devices, advances in physiological explorations have enabled us to move from the classic but still widely used cardiopulmonary exercise test (CPET) to the collection of data under real conditions on…
Using unsupervised machine learning to characterize recovery patterns in elite canoe-kayak athletes across the Olympic training year
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 5,85…
Athletes (1 work) · Cluster analysis (1 work) · Discriminator (1 work) · Elite Athletes (1 work) · Heart Rate Variability and Autonomic Control (1 work) · Respiratory Rate (1 work) · Sleep (system call (1 work) · Sleep and related disorders (1 work) · Sleep and Work-Related Fatigue (1 work) · Sleep apnea (1 work)