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<p>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, <i>p</i> &lt; 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.</p>

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Optimizing ergonomic risk assessment using fuzzy irregular cellular automata: a novel approach to modeling musculoskeletal disorders in industrial workstations

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年12月29日

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  • Mostafa Kashani,
  • Asma Zare,
  • Seddigheh Barzekar,
  • Masoud Bagheri Jaamebozorgi,
  • Mohammad Ali Moradpour,
  • Mojtaba Sadeghi

摘要

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.