Ergonomic Risk Assessment

Ergonomic risk assessment aims to identify and mitigate physical hazards in workplaces, primarily focusing on preventing musculoskeletal disorders. Current research emphasizes data-driven approaches, utilizing machine learning models (like convolutional neural networks) to analyze diverse data sources such as video recordings of worker movements and sensor data capturing hand and body postures and forces, often incorporating established ergonomic scoring systems (e.g., RULA, HAL) and developing novel metrics for more granular risk assessment. These advancements enable more objective, comprehensive, and potentially automated assessments, leading to improved workplace safety and reduced worker injury.