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

Sang Hyeon Kang et al.

IISE Transactions on Occupational Ergonomics and Human Factors2026https://doi.org/10.1080/24725838.2026.2646995article
AJG 1
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0.50

What the paper says

OCCUPATIONAL APPLICATIONSThis study investigated whether tree-based machine learning algorithms can predict objective (EMG-based) and subjective (Borg CR10) low back fatigue during a sustained trunk flexion, with and without back-support exosuit assistance, using postural control features. The results revealed that all tree-based algorithms reasonably predicted objective and/or subjective fatigue during sustained trunk flexion (F1-score range: 74.1%-97.7%), with Extra Trees achieving the highest mean F1-score (91.4%). Fatigue detection was more accurate with subjective or combined fatigue labeling than with objective fatigue labeling alone, and exosuit assistance improved model precision and F1-score. Feature selection identified vertical peak and mean ground reaction forces as the most influential predictors. These results suggest that noninvasive postural control monitoring via smart insoles or force-sensing shoes, combined with machine learning, can enable real-time assessment of fatigue during trunk flexion, supporting timely ergonomic interventions and workload adjustments, or exoskeleton use to mitigate the risk of low back disorders.

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https://doi.org/https://doi.org/10.1080/24725838.2026.2646995

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@article{sang2026,
  title        = {{A Machine Learning Approach to Predict Objective and Subjective Low Back Fatigue Using Postural Control Features During Sustained Trunk Flexion}},
  author       = {Sang Hyeon Kang et al.},
  journal      = {IISE Transactions on Occupational Ergonomics and Human Factors},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1080/24725838.2026.2646995},
}

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

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