DOI: 10.5281/zenodo.17871342
title: Fatigue Monitoring in Futsal Athletes using Physiological Wearable Sensors

description: 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. The goal of this dataset is to support the development of machine learning models for fatigue monitoring and performance evaluation using wearable sensor data.

The data collection took place at the futsal stadium ("Stadio, Tensostatico Antistadio - A. Montinaro - Via del Mare, 73100 - Lecce") where athletes of the Lecce futsal team train and compete regularly. In addition, the experiments have been authorized from the Italian Ethical Committee through the protocol n. 1752/CEL - Studio IoTSport2024. Each participant signed a written informed consent describing the purpose, methodology, and minimum risks of the experiments, and they were explicitly informed about the implications of monitoring during training sessions. 

All data have been anonymized in compliance with the European General Data Protection Regulation (GDPR).

 

When using this resource, please cite:

A. Mamen, E. De Giovanni, T. Montanaro, I. Sergi, L. Patrono, "Human-Centered and Context-Aware Smart ML-based IoT Framework for Online Fatigue Detection: a Real-World Study of Football Training", Internet of Things, 2025, 101847, ISSN 2542-6605, DOI: 10.1016/j.iot.2025.101847

Data Description



Demographic data



Player ID, Age, Height (cm), Weight (kg), Playing Position, Smoking Status.


Physiological data



Time-domain features

Non-linear features




%HR Reserve, HR Index, Net HR, VO₂ estimates





Heart Rate (HR) (from ECG and PPG)

RR Intervals (ms, from ECG)

Heart Rate Variability (HRV) Features

Heart Rate-derived Features


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


 File description

The following list summarizes the content of the attached files:



raw data.zip: it contains all the raw data collected in the experiments.

data for training.zip: It contains the data used for the training of the ML algorithms used within the paper: A. Mamen, E. De Giovanni, T. Montanaro, I. Sergi, L. Patrono, "Human-Centered and Context-Aware Smart ML-based IoT Framework for Online Fatigue Detection: a Real-World Study of Football Training", Internet of Things, 2025, 101847, ISSN 2542-6605, DOI: 10.1016/j.iot.2025.101847

code for reading data for training.py: it contains the python code that researchers can use or readapt to read data

Data treated.zip: it contains the data obtained after pre-processing phase described in the published paper (DOI: 10.1016/j.iot.2025.101847).

"training table.zip": It reports the list of all the performed and monitored training exercises as detailed in the following table.


 

Training Exercises (reported in "training Table 1.zip")




 
ExerciseId 
Exercise Description
Duration (minutes )


1st session 
125
Dynamic Team Warm-up
15


 
 
Resting
2


 
127
metabolic training
15


 
 
Resting
2


 
129
strength transformation phase 
20


2nd session
130
Core stability 
4


 
 
Resting
2


 
132
Strength & Agility Warm-Up
15


 
 
Resting
2


 
134
Metabolic circuit 
25


3rd session
136
Full-Body Warm-Up
15


 
 
Resting
2


 
138
Reactive speed training 
15


 
 
Resting
2


 
140
Game-situation tactics 
15


4th session
141
Core Endurance 
5


 
 
Resting
2


 
143
Progressive Speed Warm-Up
10


 
 
Resting
2


 
145
Dry aerobic training 
16


5th session
146
General Warm-up
20


 
 
Resting
2


 
148
Agility & Acceleration Circuit 
5


 
 
Resting
2


 
150
Reactive Sprint Drill
4


 
 
Resting
2


 
152
Max Sprint End-to-Mid Drill 
4




 

Keywords

Fatigue detection, Heart rate variability, RR intervals, Wearable sensors, Polar H10, Polar Verity Sense, PPG, ECG, Athletes, Sports science