An AI-Powered Model for Automatic Real-Time Assessment of Seated Work Postures Using Rapid Upper Limb Assessment (RULA)

Abstract

Improper seated work postures are common in the workplace and can lead to various musculoskeletal problems, ranging from joint pain to permanent disability. Continuous assessment of seated work postures is necessary to help prevent these risks. However, existing studies have often relied on rule-based methods, which are highly vulnerable to measurement noise from motion capture devices. To overcome this limitation, this study proposes a learning-based approach for posture assessment. Ten participants were recruited to mimic the seven most common awkward and potentially risky seated work postures, while joint angles of their upper body were recorded and computed using both an RGB video-based approach and a Vicon motion capture system-based approach. The RGB measurements were used as features, while the Vicon measurements were used to derive accurate reference labels by comparison against Rapid Upper Limb Assessment (RULA) criteria. A multi-output Random Forest classifier was trained to predict joint-level posture assessment scores, and the model performance was evaluated using a leave-one-subject-out cross-validation scheme. The results provide initial evidence that the model achieved high performance in neck score assessment, while the trunk, shoulder, and elbow scores were more sensitive to posture types and inter-subject differences in posture execution. A larger dataset with more posture types or more subjects would improve the robustness and generalizability of the model.