Empowering industry 5.0: automated sensor-based ergonomic risk assessment

The prevention of work-related musculoskeletal disorders (WRMSDs) is a critical priority in the transition to Industry 5.0, which emphasizes human-centric, resilient, and sustainable industrial practices. Observational methods commonly used to assess ergonomic risks in the workplace often fall short due to inefficiency, subjectivity, and time-consuming procedures. These traditional approaches typically require ergonomists to estimate critical parameters, such as joint angles or task repetition rates, based on visual observation or video analysis, which can introduce potential biases and inconsistencies. This study proposes a novel framework that leverages wearable sensor technologies to automate and enhance the ergonomic risk assessment process. By integrating data from motion capture systems, mocap gloves, electromyography sensors, and other devices, the approach provides precise, real-time measurements while minimizing the need for manual input to only well-known parameters. The manuscript includes a comprehensive mapping of parameters that can be automatically collected based on recognized standards (e.g., RULA, OCRA, and NIOSH) and outlines the associated computational complexity. Ergonomic indices are generated automatically, ensuring consistent and objective evaluations. The performance of the sensor-based process is evaluated through multiple case studies, comparing it with traditional methods. While the potential of sensor-based approaches to deliver more objective and reliable ergonomic risk assessments is widely acknowledged, this work specifically examines their feasibility for automation and quantifies the benefits in terms of process efficiency. The proposed methodology not only supports the accurate identification of ergonomic risks but also empowers a proactive, worker-focused strategy for occupational health.