Computer-Vision-Enabled Worker Video Analysis for Motion Amount Quantification

• URL: http://arxiv.org/abs/2405.13999v2
• Date: Tue, 19 Nov 2024 07:45:30 GMT
• Title: Computer-Vision-Enabled Worker Video Analysis for Motion Amount Quantification
• Authors: Hari Iyer, Neel Macwan, Shenghan Guo, Heejin Jeong,
• Abstract summary: This paper introduces a novel framework for tracking and quantifying upper and lower limb motions. Using joint position data from posture estimation, the framework employs Hotelling's $T2$ statistic to quantify and monitor motion amounts. The results indicate that the correlation between workers' joint motion amounts and Hotelling's $T2$ statistic is approximately 35% higher for micro-tasks than macro-tasks.
• Score: 2.7523980737007414
• License:
• Abstract: 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.

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