What question did this study set out to answer?

The aim is to understand how spinal cord injury affects skin temperature regulation during exercise using predictive models.

February 6, 2026

Spinal cord injury impact on skin temperature regulation during graded exercise: metaheuristic data mining-based predictions

Key Points

The aim is to understand how spinal cord injury affects skin temperature regulation during exercise using predictive models.
Applied hybrid extreme learning machine models optimized with various algorithms.
Participants included 16 with spinal cord injury and 16 controls during graded arm-crank exercise.
Analyzed temperature dynamics using feature-importance and SHAP analyses.
The Dragonfly-optimized ELM model demonstrated 99.705% accuracy in predicting core temperature.
No significant difference between predicted and measured core temperatures (p > 0.05).
Peak power output, group, and stage were identified as significant predictors of temperature dynamics.

Abstract

People with spinal cord injury (SCI) show impaired thermoregulation during exercise, making skin temperature a noninvasive indicator. This study applies hybrid Extreme Learning Machine (ELM) models optimized with Ant Lion, Dragonfly, and Evolution Strategy algorithms to predict skin and core temperature dynamics during graded arm-crank exercise in 32 participants (16 SCI, 16 controls). The Dragonfly-optimized ELM achieved the highest accuracy (R² = 99.705, RMSE = 0.014) with no significant difference between predicted and measured core temperatures (p > 0.05). Feature-importance and SHAP analyses identified peak power output, group, and stage as dominant predictors, indicating reduced peripheral thermoregulatory variability in SCI.

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Cite This Study

Yang et al. (Wed,) studied this question.

synapsesocial.com/papers/698585fe8f7c464f23009cbb https://doi.org/https://doi.org/10.1080/10255842.2025.2612530

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