Fuzzy Logic Modelling of Risk Variables Influencing Musculoskeletal Disorders in Carpet Industry: A Cross-Sectional Study.

Objective: The purpose of this study was to develop a fuzzy prediction model that could help in determining the musculoskeletal risk involved in the occupation of hand-made carpet weaving.

Methods: A questionnaire-based study involving 193 carpet weavers in Jammu and Kashmir was conducted. The questionnaire collected information on demographics, psychosocial factors, workplace fatigue, and musculoskeletal complaints. A rapid entire-body assessment technique was also used to assess the weaver's working posture for musculoskeletal risk. A fuzzy logic model was used to determines the degree to which a proposition is true or untrue rather of categorizing it as absolute truth (1) or untruth (0). This technique provides for a more subtle examination of accuracy, taking into account the variety of variables and levels that exist between standard binary classification.

Results: Work stress, socio-emotional factors, family-related responsibilities, lack of motivation, sleepiness, lack of energy, physical exertion, and discomfort were found to have a statistically significant relationship with musculoskeletal complaints. Between real and predicted musculoskeletal complaints, a correlation coefficient of 0.46 was calculated. Using the REBA for postural analysis, HSEJSQ for psychosocial job parameters, and SOFI for fatigue, significant predictors of musculoskeletal complaints were identified and analyzed using fuzzy logic. The fuzzy model's predictions showed a moderate correlation with actual musculoskeletal complaints measured by the Nordic questionnaire, underscoring the impact of psychosocial and physical factors on these complaints.

Conclusion: The current model had a moderate relationship with actual musculoskeletal complaints and can be used to assess the musculoskeletal risk associated with work in a timely manner.