Real-Time Automated Ergonomic Monitoring: A Bio-Inspired System Using 3D Computer Vision

实时自动化人体工程学监测:一种基于3D计算机视觉的仿生系统

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Abstract

Work-related musculoskeletal disorders (MSDs) remain a global occupational health priority, with recognized limitations in current point-in-time assessment methodologies. This research extends prior computer vision ergonomic assessment approaches by implementing biological proprioceptive feedback principles into a continuous, real-time monitoring system. Unlike traditional periodic ergonomic evaluation methods such as "Rapid Upper Limb Assessment" (RULA), our bio-inspired system translates natural proprioceptive mechanisms-which enable continuous postural monitoring through spinal feedback loops operating at 50-150 ms latencies-into automated assessment technology. The system integrates (1) markerless 3D pose estimation via MediaPipe Holistic (33 anatomical landmarks at 30 FPS), (2) depth validation via Orbbec Femto Mega RGB-D camera (640 × 576 resolution, Time-of-Flight sensor), and (3) proprioceptive-inspired alert architecture. Experimental validation with 40 adult participants (age 18-25, n = 26 female, n = 14 male) performing standardized load-lifting tasks (6 kg) demonstrated that 62.5% exhibited critical postural risk (RULA ≥ 5) during dynamic movement versus 7.5% at static rest, with McNemar test p<0.001 (Cohen's h=1.22, 95% CI: 0.91-0.97). The system achieved 95% Pearson correlation between risk elevation and alert activation, with response latency of 42.1±8.3 ms. This work demonstrates technical feasibility for continuous occupational monitoring. However, long-term prospective studies are required to establish whether continuous real-time feedback reduces workplace injury incidence. The biomimetic design framework provides a systematic foundation for translating biological feedback principles into occupational health technology.

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