WMSDsNet: A Deep Learning Framework for Real-Time Ergonomic Risk Prediction in Human-Robot Collaboration in Disassembly

Disassembly tasks are increasingly vital for sustainable manufacturing and the circular economy, as they facilitate component recovery and waste reduction. While humanrobot collaboration (HRC) is often promoted for reducing physical ergonomic challenges compared to tasks performed entirely by humans, studies have largely overlooked the unique ergonomic issues inherent to HRC. These environments can still present challenges that, if neglected, can contribute to work-related musculoskeletal disorders (WMSDs). This study introduces WMSDsNet, a dual-head deep-learning framework that automates ergonomic risk assessment by simultaneously classifying subtasks and predicting ergonomic risks, offering realtime, cumulative risk evaluation using wearable sensor data. Unlike traditional methods, which rely on subjective and timeintensive manual observations, or previous works that primarily focus on posture-based risk assessments to recognize awkward postures for immediate alerts or feedback, WMSDsNet detects changes in posture over a specific period of time. Based on this information, the frequency and duration of awkward postures can be analyzed to understand their cumulative effects on ergonomic risks. We analyzed the task of disassembling a Programmable Logic Controller (PLC) and selected specific subtasks to be performed by human operators in collaboration with the robot, including unscrewing components, detaching cables, sorting components, and changing the cobot's tools. Data was collected in numerical form using wearable sensors, enabling the framework to evaluate risk levels and predict ergonomic risks with over 90% accuracy in task classification and risk assessment. By providing real-time ergonomic assessments, this framework supports proactive interventions, offering a significant advancement in ergonomic evaluation for industrial HRC environments.