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

2024-05-22

arXiv.org

AI Summary

To address the challenges of real-time quantification of workers’ limb motion and low accuracy in micro-task-level fatigue detection in industrial settings, this paper proposes a video-based analytical method grounded in multivariate statistical process control (SPC). First, joint keypoints are extracted from video sequences using HRNet or OpenPose, followed by construction of multidimensional motion feature vectors. Innovatively, Hotelling’s T² control chart is introduced to dynamically model and monitor motion intensity, triggering fatigue alerts when the T² statistic exceeds a predefined threshold. This work represents the first application of multivariate SPC charts to human motion quantification, significantly enhancing discrimination of subtle motion differences at the micro-task level—achieving approximately 35% higher correlation than conventional macro-task analysis. Validation in real industrial environments demonstrates millisecond-level system responsiveness and high-precision motion quantification, enabling fine-grained human factors ergonomics assessment and proactive intervention.

Technology Category

• Computer Vision: Video Understanding & Activity Analysis
• Machine Learning: Multi-instance/Multi-view Learning
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods

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.

Problem

• Quantify worker limb motions using computer vision
• Monitor motion thresholds to issue ergonomic alerts
• Assess correlation between motion warnings and workload

Innovation

• Computer vision tracks worker limb motions
• Hotelling's T² statistic quantifies motion amounts
• Random Forest model detects ergonomic risks