  The performance of physical workers is significantly influenced by the
quantity of their motions. However, monitoring and assessing these motions is
challenging due to the complexities of motion sensing, tracking, and
quantification. Recent advancements have utilized in-situ video analysis for
real-time observation of worker behaviors, enabling data-driven quantification
of motion amounts. Nevertheless, there are limitations to monitoring worker
movements using video data. This paper introduces a novel framework based on
computer vision to track and quantify the motion of workers' upper and lower
limbs, issuing alerts when the motion reaches critical thresholds. Using joint
position data from posture estimation, the framework employs Hotelling's T$^2$
statistic to quantify and monitor motion amounts, integrating computer vision
tools to address challenges in automated worker training and enhance
exploratory research in this field. We collected data of participants
performing lifting and moving tasks with large boxes and small wooden cubes, to
simulate macro and micro assembly tasks respectively. It was found that the
correlation between workers' joint motion amount and the Hotelling's T$^2$
statistic was approximately 35% greater for micro tasks compared to macro
tasks, highlighting the framework's ability to identify fine-grained motion
differences. This study demonstrates the effectiveness of the proposed system
in real-time applications across various industry settings. It provides a tool
for enhancing worker safety and productivity through precision motion analysis
and proactive ergonomic adjustments.
