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Computer-Vision-Enabled Worker Video Analysis for Motion Amount Quantification
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DOI：10.3390/s26165153.png)
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Abstract
En 中文
The performance of physical workers is significantly influenced by the extent and quality of their motions. However, accurately measuring and assessing these motions remains a challenge due to the limitations in conventional instrumentation; wearable sensors require calibration and restrict mobility, while marker-based motion capture systems are costly and impractical for field deployment. Recent advancements have enabled in situ video analysis for the real-time observation of worker behaviors. To address these measurement constraints, this paper introduces a novel framework for tracking and quantifying upper and lower limb motions, issuing alerts when critical thresholds are reached. Using joint position data from posture estimation, the framework employs Hotelling’s T 2 statistic to quantify and monitor motion amounts. A significant positive correlation was noted between motion warnings and the overall NASA Task Load Index (TLX) workload rating (r = 0.218, p < 0.005). A supervised Random Forest model trained on the collected motion data was benchmarked across multiple datasets, including the in-house assembly dataset, G-AI-HMS, UCF Sports Action, UCF50, and PE-USGC. The proposed framework identified motion anomaly patterns with a maximum accuracy of 94% on the in-house assembly dataset, while performance varied across the external benchmark datasets.
Keywords:
computer vision
Hotelling’s <i>T</i><sup>2</sup>
in situ videos
joint motion amount
posture estimation