An Ergonomic Risk Assessment System Based on 3D Human Pose Estimation and Collaborative Robot

Abstract

Human pose estimation focuses on methods that allow us to assess ergonomic risk in the workplace and aims to prevent work-related musculoskeletal disorders (WMSDs). The recent increase in the use of Industry 4.0 technologies has allowed advances to be made in machine learning (ML) techniques for image processing to enable automated ergonomic risk assessment. In this context, this study aimed to develop a method of calculating joint angles from digital snapshots or videos using computer vision and ML techniques to achieve a more accurate evaluation of ergonomic risk. Starting with an ergonomic analysis, this study explored the use of a semi-supervised training method to detect the skeletons of workers and to estimate the positions and angles of their joints. A criticality index, based on RULA scores and fuzzy rules, is then calculated to evaluate possible corrective actions aimed at reducing WMSDs and improving production capacity using a collaborative robot that supports workers in carrying out critical operations. This method is tested in a real industrial case in which the manual assembly of electrical components is conducted, achieving a reduction in overall ergonomic stress of 13% and an increase in production capacity of 33% during a work shift. The proposed approach can overcome the limitations of recent developments based on computer vision or wearable sensors by performing an assessment with an objective and flexible approach to postural analysis development.