Integrated Cognitive Workload and Musculoskeletal Exposure Modeling for Adaptive Ergonomic Work Design
Abstract Cognitive workload, musculoskeletal exposure, fatigue, and limited recovery can jointly affect worker comfort and task performance. However, these factors are often assessed separately, limiting the evaluation of cumulative ergonomic risk over time. This study proposes an integrated ergonomic modeling framework that combines cognitive workload, physical exposure, fatigue accumulation, and recovery within a common assessment structure. Normalized state variables are used to calculate a composite ergonomic risk index, and a proposed multi objective optimization procedure provides a basis for examining alternative task schedules and recovery intervals. Illustrative scenarios compare low, moderate, and high combined demands under specified model assumptions. The proposed analytical design also specifies a procedure for future sensitivity analysis of workload intensity, exposure duration, recovery rate, and worker susceptibility. The illustrative values indicate that higher modeled cognitive workload, physical exposure, and fatigue correspond to greater composite risk under the specified weighting scheme. However, these values are hypothetical and do not represent observed worker data or verified simulation outputs. The framework provides a methodological basis for examining relationships between modeled ergonomic risk and task performance during work schedule planning. Further research should implement reproducible computational experiments, conduct sensitivity analyses, and validate model predictions against measured workload, physical exposure, fatigue, and performance data before practical application.
Authors
- Sheikh Fuzael Rahman (ORCID: https://orcid.org/0009-0001-3189-9069)
Institutions
- Lamar University (US)
Publication Details
- Journal
- Research Square
- Published
- 2026-11-06
- DOI
- https://doi.org/10.21203/rs.3.rs-11626209/v1
- Primary Topic
- Occupational Health and Safety Management
- Type
- preprint