Fatigue Monitoring in Futsal Athletes using Physiological Wearable Sensors

This dataset includes demographic, physiological, and fatigue-related data collected from 8 male futsal athletes during regular training sessions between 25 July 2024 and 8 August 2024 at Stadio, Tensostatico Antistadio - A. Montinaro - Via del Mare, 73100 - Lecce, Italy. The goal of this dataset is to support the development of machine learning models for fatigue monitoring and performance evaluation using wearable sensor data. All data have been anonymized in compliance with the European General Data Protection Regulation (GDPR). Data Description Demographic data Player ID, Age, Height (cm), Weight (kg), Playing Position, Smoking Status. Physiological data Heart Rate (HR) (from ECG and PPG) RR Intervals (ms, from ECG) Heart Rate Variability (HRV) Features Time-domain features Non-linear features Heart Rate-derived Features %HR Reserve, HR Index, Net HR, VO₂ estimates Fatigue Labels Borg RPE scale values annotated by athletes and corrected by the coach, categorized into: Low, Moderate, High, and Very High fatigue. Used Devices Polar H10 ECG Sensor Polar Verity Sense PPG Sensor Smartphone app for data collection and timestamping Intended Use This dataset is intended for: Fatigue detection and classification research Machine learning and signal processing applications HRV and RR interval analysis Sports performance analytics Wearable sensor benchmarking Keywords Fatigue detection, Heart rate variability, RR intervals, Wearable sensors, Polar H10, Polar Verity Sense, PPG, ECG, Athletes, Sports science