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A simulation-based context-aware multimodal ML framework for proactive worker safety monitoring in resource-constrained factories

Year: 2026

Type: article

Abstract: Occupational safety monitoring in resource-constrained factories remains dominated by manual and reactive inspection. This study presents a simulation-based proof-of-concept evaluation of a context-aware safety decision architecture that integrates three sensing modalities - environmental measurements, proposed image-based PPE detection, and RFID zone tracking - together with incident-history context, an explicit rule table, and a designed human-in-the-loop (HITL) review layer. The evaluation uses 300 simulated observations. Because the target classes were generated by the same expert-defined rule table that maps sensor risk, PPE status, and zone level to No Alert, Warning, or High Risk, the supervised classifiers approximate that policy rather than predict independently observed accidents. Six classifiers were compared using stratified cross-validation and a held-out test partition. At the original seed-42 split, Random Forest and XGBoost produced identical held-out results (accuracy and weighted F1 = 0.833; macro F1 = 0.829; balanced accuracy = 0.800; Cohen's kappa = 0.718; High-Risk recall = 0.700). A supplementary reconstructed fixed-dataset analysis across seeds 0, 1, 42, 123, and 999 yielded held-out weighted F1 of 0.836 +/- 0.022 for Random Forest and 0.859 +/- 0.027 for XGBoost. Across five seed-level cross-validation means, the Full and Sensor+Zone configurations differed by only 0.003 +/- 0.012 weighted F1, with no significant difference detected in an exploratory paired test (W = 5.0, p = 0.625). The proposed YOLOv8 and HITL components were not empirically validated. The results therefore demonstrate policy-approximation feasibility under simulation and identify sensor and zone information as the dominant contributors, while real-factory validation remains essential.

Source: Array

DOI: 10.1016/j.array.2026.101198

Authors: Nasif Morshed, Md Saiful Islam Seam, Md Zaid Hossain, Md Al Amin

Institution: Khulna University of Engineering and Technology

Language: English

Topic: Occupational Health and Safety Research

Subfield: Radiological and Ultrasound Technology

Field: Health Professions

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $1,680