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A Machine Learning Approach to Predict Objective and Subjective Low Back Fatigue Using Postural Control Features

Sang Hyeon Kang¹, Jaejin Hwang², Mostafa Etebar Zadeh¹

¹Human Performance Institute, Department of Industrial and Entrepreneurial Engineering and Engineering Management, Western Michigan University, Kalamazoo, MI, USA.

IISE Transactions on Occupational Ergonomics and Human Factors

|March 27, 2026

Summary

This summary is machine-generated.

Machine learning accurately predicts low back fatigue during trunk flexion using postural control. Exosuit assistance enhances prediction accuracy, suggesting smart insoles for real-time ergonomic monitoring and intervention.

Keywords:

Machine learning exoskeleton fatigue

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Area of Science:

Occupational health and safety
Biomechanical engineering
Machine learning applications

Background:

Low back fatigue is a significant occupational hazard, particularly during sustained trunk flexion.
Current fatigue assessment methods are often subjective or invasive.
Exoskeleton technology offers potential for mitigating physical strain in occupational settings.

Purpose of the Study:

To evaluate tree-based machine learning algorithms for predicting objective and subjective low back fatigue.
To assess the impact of back-support exosuit assistance on fatigue prediction accuracy.
To identify key postural control features for fatigue detection.

Main Methods:

Utilized tree-based algorithms (e.g., Extra Trees) to analyze postural control features during sustained trunk flexion.
Incorporated objective (EMG) and subjective (Borg CR10) fatigue measures.
Investigated the effect of exosuit assistance on model performance.
Performed feature selection to identify critical predictors.

Main Results:

All tested algorithms demonstrated reasonable to high accuracy (F1-scores 74.1%-97.7%) in predicting fatigue.
Extra Trees algorithm yielded the highest mean F1-score (91.4%).
Subjective or combined fatigue labeling improved prediction over objective labeling alone.
Exosuit assistance enhanced model precision and F1-score.
Vertical ground reaction forces were identified as key predictors.

Conclusions:

Noninvasive postural control monitoring via smart insoles or force-sensing shoes, coupled with machine learning, can enable real-time fatigue assessment.
This approach supports timely ergonomic interventions and workload adjustments.
Exoskeleton use can be optimized based on real-time fatigue data to mitigate low back disorder risks.