Validation of computer vision-based ergonomic risk assessment tools for real manufacturing environments.

Sci Rep

Department of Industrial Engineering and Mathematical Sciences, Università Politecnica delle Marche, via Brecce Bianche, 12, Ancona, 60131, AN, Italy.

Published: November 2024

AI Article Synopsis

  • - This study develops a semi-automated tool for ergonomic risk assessments in manufacturing, focusing on improving worker safety and efficiency in the context of Industry 5.0, where human-centered approaches and digital technologies are essential due to an aging workforce.
  • - The research emphasizes the need for continuous monitoring of workers' postures to reduce work-related musculoskeletal disorders (MSDs), arguing that traditional ergonomic assessment methods are insufficient in dynamic work environments.
  • - Using a benchmarked low-cost 2D RGB Motion Capture (MoCap) system, the study tests its effectiveness for assessing ergonomic risks in various manufacturing settings, finding that while accurate assessments depend on specific environmental factors, the technology shows promise for enhancing safety.

Article Abstract

This study contributes to understanding semi-automated ergonomic risk assessments in industrial manufacturing environments, proposing a practical tool for enhancing worker safety and operational efficiency. In the Industry 5.0 era, the human-centric approach in manufacturing is crucial, especially considering the aging workforce and the dynamic nature of the entire modern industrial sector, today integrating digital technology, automation, and sustainable practices to enhance productivity and environmental responsibility. This approach aims to adapt work conditions to individual capabilities, addressing the high incidence of work-related musculoskeletal disorders (MSDs). The traditional, subjective methods of ergonomic assessment are inadequate for dynamic settings, highlighting the need for affordable, automatic tools for continuous monitoring of workers' postures to evaluate ergonomic risks effectively during tasks. To enable this perspective, 2D RGB Motion Capture (MoCap) systems based on computer vision currently seem the technologies of choice, given their low intrusiveness, cost, and implementation effort. However, the reliability and applicability of these systems in the dynamic and varied manufacturing environment remain uncertain. This research benchmarks various literature proposed MoCap tools and examines the viability of MoCap systems for ergonomic risk assessments in Industry 5.0 by exploiting one of the benchmarked semi-automated, low-cost and non-intrusive 2D RGB MoCap system, capable of continuously monitoring and analysing workers' postures. By conducting experiments across varied manufacturing environments, this research evaluates the system's effectiveness in assessing ergonomic risks and its adaptability to different production lines. Results reveal that the accuracy of risk assessments varies by specific environmental conditions and workstation setups. Although these systems are not yet optimized for expert-level risk certification, they offer significant potential for enhancing workplace safety and efficiency by providing continuous posture monitoring. Future improvements could explore advanced computational techniques like machine learning to refine ergonomic assessments further.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11561082PMC
http://dx.doi.org/10.1038/s41598-024-79373-4DOI Listing

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