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Inthis study, a fuzzy-based expert system called the Pain Intensity PredictionExpert System (PIPES) was developed to predict pain severity risk (PSR) inshoveling-related tasks. The primary objective was to develop a non-changing rating risk assessment ergonomicstool that both efficientand comparable with those obtained from human ergonomics experts in the fieldof application. PIPES used fuzzy settheory (FST) to make decisions about the level of pain associated with aselected worker base on the measured task variables, namely scooping rate, scooping time, shovel load, andthrow distance as input and PSR as the result. Values obtained from variable measurements from a sand shoveling taskwere run with PIPES, and the results were compared with the workers?self-reported pain (WSRP) intensity using a numeric rating scale (NRS) tool. The result of validation showed that there was astrong positive relationship between WSRP NRS and PIPES NRS, with a correlationcoefficient of 0.70. The independent sample t-test for mean difference showed that WSRP had a statistically significantly lower level of NRS (4.35 ± 2.1)compared to PIPES (4.75 ± 2.2), t (38) = - 0.591, p = 0.558. With asignificance level of 0.001 at 95% confidence, the groups? means were notsignificantly different. The study developed an expert system, PIPES, which can be used as a computerizedrepresentation of ergonomics experts, who are scarce. PIPES can be applied to construction industries, sand mine locations,and any workplace where materials are manually moved using a shovel. |
