The performance of physical workers is significantly influenced by the extent of their motions. However, monitoring and assessing these motions remains a challenge. Recent advancements have enabled in-situ video analysis for real-time observation of worker behaviors. 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. The results indicate that the correlation between workers' joint motion amounts and Hotelling's $T^2$ statistic is approximately 35\% higher for micro-tasks than macro-tasks, demonstrating the framework's ability to detect fine-grained motion differences. This study highlights the proposed system's effectiveness in real-time applications across various industry settings, providing a valuable tool for precision motion analysis and proactive ergonomic adjustments.

翻译：体力劳动者的表现显著受其运动幅度的影响。然而，对这些运动的监测与评估仍具挑战性。近期的技术进步已能实现用于实时观察工人行为的原位视频分析。本文提出了一种新颖的框架，用于追踪和量化上下肢运动，并在达到关键阈值时发出警报。该框架利用姿态估计获得的关节位置数据，采用霍特林$T^2$统计量来量化与监测运动量。结果表明，对于微任务而言，工人关节运动量与霍特林$T^2$统计量之间的相关性比宏任务高出约35%，这证明了该框架检测细粒度运动差异的能力。本研究凸显了所提系统在各种工业场景中实时应用的有效性，为精确运动分析和主动的人机工程学调整提供了一个有价值的工具。