LightPose: A lightweight fatigue-aware pose estimation framework
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".