ArticleScientific reports2025
Optimizing ergonomic risk assessment using fuzzy irregular cellular automata: a novel approach to modeling musculoskeletal disorders in industrial workstations.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Musculoskeletal disorders (MSDs) and structural musculoskeletal abnormalities cause substantial work-related pain, disability, and productivity loss in industrial workforces; traditional screening tools and single-variable analyses can miss complex, interdependent risk patterns that arise from combined biomechanical exposures, workstation mismatches, and worker characteristics. We aimed to develop and validate a data-driven framework - Fuzzy Irregular Cellular Automata (FICA) - to integrate multi-modal ergonomic measurements, detect high-risk abnormality clusters, quantify their associations with MSDs and demographic predictors, and prioritize targeted ergonomic interventions. Comprehensive data were collected from 415 supervisory/administrative workers (15 objectively measured abnormalities; self-reported MSDs via the Nordic questionnaire; workstation anthropometry; exposure-time logs). FICA represented the inputs as a fuzzy graph and applied Mamdani inference with a voting/colouring routine (FICAVCA) to identify clusters and rank interventions. Model validation used repeated 10-fold cross-validation and benchmarking against alternative algorithms. FICA identified interpretable high-risk clusters (e.g., lumbar lordosis + dropped shoulder, 22.2%) and demonstrated strong predictive performance (accuracy = 0.92; stability index = 0.89). Multivariate analyses confirmed BMI, age, and work experience as key predictors (R² = 0.43, p < 0.001); BMI ≥ 30 increased odds of lumbar lordosis (OR = 2.3) and genu varum (OR = 1.9). Model-based intervention simulations estimated prioritized workstation and exercise interventions could reduce cluster risk by up to ≈ 34% (scenario projections). FICA provides an interpretable, scalable method to translate multimodal ergonomic data into prioritized, resource-efficient interventions. Longitudinal trials are needed to validate projected intervention effects.
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