Abstract

Work-related musculoskeletal disorders is a major problem for worker's health when dealing with repetitive tasks. The Occupational Repetitive Actions Index (OCRA) is one of the most widely used methods for determining upper extremity risk from repetitive actions. Traditional risk assessment consists in observing and recording the entire workday or part of it. This observational assessment depends on the evaluator's expertise, which lead to inter-evaluator variability in the results even for the same job. This paper presents preliminary results of a platform that combines motion capture data with a digitalized OCRA Index method to classify risks under repetitive tasks conditions. Repetitive action tasks were performed in a controlled laboratory environment on ten healthy subjects, where optical and inertial sensors were used to capture movement. Body segment positions, displacements and joint angles were fed into the platform comparing their processing time and reliability. Although results indicate that optical systems have a better performance than inertial sensors for posture evaluation, the last has better practical applicability since optical markers occlusion in the working setup induced measurement errors. In addition, inertial systems are more likely to be used on in real working scenarios due to their ease of use and portability. Finally, inertial sensors combined with the digitized OCRA index method platform effectively reduces 67% of the needed evaluation time compared to traditional observational methods.