Player Tracking Data and Psychophysiological Features Associated with Mental Fatigue in U15, U17, and U19 Male Football Players: A Machine Learning Approach

Player Tracking Data and Psychophysiological Features Associated with Mental Fatigue in U15, U17, and U19 Male Football Players: A Machine Learning Approach

Optimizing recovery is crucial for maintaining performance and reducing fatigue and injury risk in youth football players. This study applied machine learning (ML) models to classify mental fatigue in U15, U17, and U19 male players using wearable signals, tracking data, and psychophysiological featu...

Full description

Saved in:
Bibliographic Details
Main Authors: José E. Teixeira, Pedro Afonso, André Schneider, Luís Branquinho, Eduardo Maio, Ricardo Ferraz, Rafael Nascimento, Ryland Morgans, Tiago M. Barbosa, António M. Monteiro, Pedro Forte
Format: Article
Language:English
Published: MDPI AG 2025-03-01
Series:Applied Sciences
Subjects:
youth
monitoring
technology
AI
psychophysiology
Online Access:https://www.mdpi.com/2076-3417/15/7/3718
Tags: Add Tag
No Tags, Be the first to tag this record!

Internet

https://www.mdpi.com/2076-3417/15/7/3718

Similar Items