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LightPose: A lightweight fatigue-aware pose estimation framework
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DOI：10.1016/j.jii.2025.100988.png)
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Abstract
En 中文
Fatigue assessment based on human motion plays a critical role in human-centric intelligent manufacturing, intelligent monitoring, and ergonomics. This growing demand underscores the need for low-cost, high-precision pose estimation techniques with broad application adaptability. To meet these requirements, we propose LightPose, a lightweight human pose estimation framework guided by bone segment principles. LightPose is designed to balance spatial accuracy with computational efficiency, delivering pose quality comparable to recent sequence-based baselines while remaining lightweight enough for real-time, fatigue-aware analysis. The framework incorporates a dual-stream supervision mechanism that enforces local geometric consistency through mutual prediction between joint pairs on the same bone segment. Additionally, kinematic constraints and fatigue-relevant metric regulations are embedded within the training objective, promoting biomechanical plausibility and alignment with fatigue-related motion patterns. Experimental results on standard 3D pose estimation benchmarks demonstrate that LightPose delivers competitive accuracy with reduced computational cost. Further evaluations confirm its effectiveness in estimating fatigue-related kinematic indicators, establishing its suitability for fatigue detection tasks. By effectively bridging efficiency and biomechanical relevance, LightPose presents a promising front-end solution for fatigue-aware motion analysis in manufacturing settings.
Keywords:
Human pose estimation
Fatigue assessment
Kinematic constraints
Lightweight models
Smart manufacturing
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