A fuzzy logic approach to improve the sensitivity of the rapid entire body assessment method.

Abstract

Conventional ergonomic observation methods, such as rapid entire body assessment (REBA), are limited in their sensitivity and reliability, particularly in detecting changes in input variables. This study integrates fuzzy logic with the REBA method, utilizing trapezoidal membership functions to fuzzify the input variables. The center of gravity method was employed for defuzzification, and if-then rules were formulated to enhance the REBA method. The findings revealed that the fuzzy REBA method is more sensitive to changes in the risk of work-related musculoskeletal disorders (WMSDs) than conventional REBA. Furthermore, the intraclass correlation coefficient (ICC) of the risk scores obtained with the fuzzy REBA method ranged from 0.76 to 0.89, indicating reliable outcomes. The proposed fuzzy REBA method offers a more reliable approach for on-site ergonomic assessments, thereby reducing observational errors and potentially reducing the incidence of WMSDs in the workplace.