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Artificial Intelligence (AI) and Sensors in Sports Safety and NextGen Rehabilitation: 2nd Edition

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Special Issue Editors

Special Issue Information

Dear Colleagues,

We are pleased to announce the second edition of this Special Issue of Sensors and to invite you to submit a manuscript. You can view the original Special Issue here: https://www.mdpi.com/journal/sensors/special_issues/707PLNH98Y.

Sensors, sensing networks, artificial intelligence, and other new and advanced technologies determine the constant and quick changes in sport performances. Sensors can help to predict and prevent injuries in athletes by giving real-time data that can be used to find possible injury risks and take the proper steps to prevent or minimize injuries. Sensors and AI-based models predict and prevent injuries by providing real-time data that can be used to control fatigue, identify injury risks, track recovery progress, and change training programs to reduce the risk of injury. Nowadays, artificial intelligence is becoming a more disruptive technology in sports safety, sports physiotherapy, injury prevention, fatigue, and recovery control by providing performance sports personnel (coaches, trainers, and medical staff) with the tools they need to monitor athletes in real time, identify potential injury risks, and develop personalized training programs and treatment plans to maximize sports safety in a supportive but invisible manner. For this Special Issue, we are looking for articles on monitoring of workload, detection of movement patterns and irregularities, methods of identifying muscle imbalances, approaches to keeping track of the recovery phase, use of next-generation physiotherapy methods, models, and sensors, and concussions from wearable sensors or remote solutions.

Dr. Attila Biró
Dr. Sándor Miklós Szilágyi
Dr. László Szilágyi
Prof. Dr. Antonio Ignacio Cuesta Vargas
Guest Editors

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Keywords

predictive modeling
injury diagnosis
fatigue management
biomechanical sensors
virtual rehabilitation
wearable sensors
radar sensors
concussion detection
smart insoles
EMG sensors
movement sensors
explainable AI (XAI) in sports diagnostics
neuromusculoskeletal (NMS) modeling
digital twin of athlete

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28 pages, 1407 KB

Open AccessArticle

Bioinformatics-Inspired IMU Stride Sequence Modeling for Fatigue Detection Using Spectral–Entropy Features and Hybrid AI in Performance Sports

by Attila Biró, Levente Kovács and László Szilágyi

Sensors 2026, 26(2), 525; https://doi.org/10.3390/s26020525 - 13 Jan 2026

Viewed by 1050

Abstract

Wearable inertial measurement units (IMUs) provide an accessible means of monitoring fatigue-related changes in running biomechanics, yet most existing methods rely on limited feature sets, lack personalization, or fail to generalize across individuals. This study introduces a bioinformatics-inspired stride sequence modeling framework that integrates spectral–entropy features, sample entropy, frequency-domain descriptors, and mixed-effects statistical modeling to detect fatigue using a single lumbar-mounted IMU. Nineteen recreational runners completed non-fatigued and fatigued 400 m runs, from which we extracted stride-level features and evaluated (1) population-level fatigue classification via global leave-one-participant-out (LOPO) models and (2) individualized fatigue detection through supervised participant-specific models and non-fatigued-only anomaly detection. Mixed-effects models revealed robust and multidimensional fatigue effects across key biomechanical features, with large standardized effect sizes (Cohen’s d up to 1.35) and substantial variance uniquely explained by fatigue (partial R² up to 0.31). Global LOPO machine learning models achieved modest accuracy (55%), highlighting strong inter-individual variability. In contrast, personalized supervised Random Forest classifiers achieved near-perfect performance (mean accuracy 97.7%; mean AUC 0.997), and NF-only One-Class SVMs detected fatigue as a deviation from individual baseline patterns (mean AUC 0.967). Entropy and stride-to-stride variability metrics further demonstrated consistent fatigue-linked increases in movement irregularity and reduced neuromuscular control. These findings show that IMU stride sequences contain highly informative, fatigue-sensitive biomechanical signatures, and that combining bioinformatics-inspired sequence analysis with hybrid statistical and personalized AI models enables both robust population-level insights and highly reliable individualized fatigue monitoring. The proposed framework supports future integration into sports analytics platforms, digital coaching systems, and real-time wearable fatigue detection technologies. This highlights the necessity of personalized fatigue-monitoring strategies in wearable systems. Full article

(This article belongs to the Special Issue Artificial Intelligence (AI) and Sensors in Sports Safety and NextGen Rehabilitation: 2nd Edition)

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