Ergonomic posture assessment and tracking for industrial cyber-physical-human systems: A case study in the heavy metalworking industry

Abstract

Abstract: In the context of Industry 5.0, collaborative automation and decision support through ubiquitous digital processing provide a privileged ap-proach to examining the human factor in the industry. At the same time, new technologies that have been under development since the in-ception of Industry 4.0, such as IoT devices, artificial vision systems, image processing algorithms, artificial intelligence, and others, have brought important opportunities to the industry, which has historically based its process transformation and management on a preventive ap-proach that helps improving decision-making. Musculoskeletal disor-ders in industrial working scenarios are often associated with the accu-mulation of stress over time, which can impact the muscles, tendons, ligaments, joints, and other parts of the body. To prevent injuries and complications in the short, medium, or long term, Cyber-Physical-Human Systems (CPHS) can be adopted to correct risky actions of op-erators in real-time. This work presents preliminary results regarding the study, understanding, and identification of operator postures in the heavy metalworking industry. The study was based on the comparison of the commonly used methods for ergonomic posture and movement assessment. The adopted approach takes advantage of computer vi-sion for operator pose identification and tracking to effectively detect the most frequently repeating body postures. The most repeated pos-tures are then categorized according to their ergonomic compatibility. To evaluate the proposed approach, a dataset has been acquired based on the observation of real operator actions. Based on the results, the implemented system enables us to actively evaluate the appropriate-ness of workers' postures in real-time.