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