Biomechanical Fuzzy Model for Analysing the Ergonomic Risk Level Associated with Upper Limb Movements

Abstract

This study proposes a decision support system that uses a fuzzy logic model to assess the risk level associated with repetitive upper limb movements during work tasks, which can lead to musculoskeletal disorders. The model considers three main sets: biomechanics, anthropometrics, and productivity. Standardised parameters were utilised to determine the risk level associated with movement. To validate the findings, a fuzzy model was applied to assess 123 female workers across three automatic high-speed production lines as a case study. The model quantifies the risks using 54 membership equations and incorporates nine linguistic variables organised into three sets: biomechanical: this includes applied force, moment force, and angle of the torso from vertical; anthropometric: this includes workers’ age and height and body mass index; and productivity: this includes working area depth, exposure time, and repetitiveness. The resulting fuzzy model, which is based on fuzzy set theory, utilises only four general fuzzy rules and allows for the evaluation of multiple workers simultaneously, providing a competitive advantage over models that rely on a large number of individual fuzzy rules to assess just one worker. The biomechanical set evaluates applied force and moment force based on productivity factors. Consequently, the behaviour of the group of 123 evaluations changed as the productivity risk value was introduced. For instance, in Test 1, which involves a low-risk task, we observed a biomechanical risk pattern that was solely related to the worker’s anthropometry. In Test 2, which presents a medium risk, the pattern of evaluations shifted, revealing behaviours that were more influenced by both anthropometric and biomechanical characteristics. Finally, in Test 3, the impact of anthropometry and biomechanics was clear in the risk assessment patterns, which aligned closely with the anthropometric. The DSS could help improve policies and work conditions.