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Bioinformatics-Inspired IMU Stride Sequence Modeling for Fatigue Detection Using Spectral-Entropy Features and Hybrid

Attila Biró1,2,3,4,5, Levente Kovács1,6, László Szilágyi1,4,6

  • 1Physiological Controls Research Center, Obuda University, 1034 Budapest, Hungary.

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|January 28, 2026
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Summary
This summary is machine-generated.

This study introduces a novel framework using wearable sensors to detect running fatigue by analyzing stride sequences. Personalized AI models accurately identify fatigue, outperforming general approaches and enabling real-time monitoring.

Keywords:
1D-CNNIMUanomaly detectionbioinformatics-inspired sequence modelingfatigue detectionhybrid AIinertial measurement unitmachine learningmixed-effects modelingrunning biomechanicssample entropyspectral analysisstride segmentationwearable sensors

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Area of Science:

  • Biomechanics
  • Wearable Technology
  • Data Science

Background:

  • Wearable inertial measurement units (IMUs) offer accessible running biomechanics monitoring.
  • Existing methods often lack personalization and struggle with inter-individual variability.
  • Fatigue detection is crucial for performance optimization and injury prevention.

Purpose of the Study:

  • To develop a bioinformatics-inspired framework for fatigue detection using a single lumbar-mounted IMU.
  • To integrate spectral-entropy, sample entropy, and frequency-domain features with statistical modeling.
  • To evaluate both population-level and individualized fatigue detection models.

Main Methods:

  • Collected stride-level biomechanical data from 19 recreational runners during non-fatigued and fatigued states.
  • Employed mixed-effects statistical models to analyze fatigue effects on biomechanical features.
  • Developed and compared global leave-one-participant-out (LOPO) models, personalized supervised Random Forest classifiers, and non-fatigued-only One-Class SVMs.

Main Results:

  • Mixed-effects models showed significant, multidimensional fatigue effects (Cohen's d up to 1.35, partial R² up to 0.31).
  • Global LOPO models had modest accuracy (55%), indicating high inter-individual variability.
  • Personalized Random Forest models achieved high accuracy (97.7%) and AUC (0.997); SVMs achieved AUC 0.967.
  • Increased movement irregularity and reduced neuromuscular control were linked to fatigue.

Conclusions:

  • IMU stride sequences contain fatigue-sensitive biomechanical signatures.
  • A hybrid approach combining sequence analysis with personalized AI models enables reliable individualized fatigue monitoring.
  • The proposed framework has potential for sports analytics, digital coaching, and real-time wearable fatigue detection.