Keykaous Azrah, Mohsen Poursadeghiyan, Mohammad Javad Fani , Mohammad Rezazade, Ardalan Solaimanian,
Volume 6, Issue 3 (9-2016)
Abstract
Introduction: Limited studies have been done to evaluate the whole-body vibration (WBV) exposure experienced by Taxi drivers. Therefore, the aim of this study was to evaluate the exposure to whole body vibration and repeated shocks in urban taxi drivers and also to compare different methods of evaluation in this job environment.
Material and Method: Measurement and evaluation process were conducted in accordance with procedure of the ISO 2631-1 and ISO 2631-5 standards. The measurements were done by SVAN 958 Sound and Vibration Analyzer and using tri-axial accelerometer centered on the contact surface between the seat and the driver in 9 taxis.
Result: The measurements done according to ISO 2631-1 method showed greater risk compared to Daily Equivalent Static Compression Dose, Sed, presented in ISO 2631-5. Calculated daily exposure durations for exposure action level in root-mean square, vibration dose value, and daily equivalent static compressive stress methods were 4.55, 3.54 and 31.70 hours, respectively.
Conclusion: The large differences in estimated exposure durations of action limits and permissible limits resulted by different methods reflect the inconsistency of the selected evaluation methods. Therefore, future research is necessary to amend the limits presented in the standard.
Roghayeh Esmali, Elham Akhlaghi Pirposhteh, Ali Askari, Mohsen Poursadeghiyan,
Volume 15, Issue 3 (10-2025)
Abstract
Introduction: Artificial Intelligence (AI) and digitalization are pivotal in enhancing Occupational Health and Safety (OHS), reducing workplace accidents, improving conditions, and boosting organizational productivity. This study examines the impacts, challenges, and opportunities of these technologies in workplace safety.
Material and Methods: A narrative review was conducted via databases (Google Scholar, PubMed, IEEE Xplore, ScienceDirect) using keywords like “AI in occupational safety” (2013–January 2025). After screening 125 articles, 71 met the inclusion criteria (Persian or English publications). Qualitative content analysis identified key challenges and opportunities.
Results: Artificial intelligence has been used in predicting incidents, monitoring, process optimization, and analyzing OHS challenges. By analyzing historical data and hazard patterns, AI enables proactive risk mitigation. Continuous learning in AI models enhances predictive accuracy and environmental adaptability. However, data quality issues persist; techniques such as transfer learning offer potential solutions. AI-driven automation reduces human error, yet challenges include ethical concerns and infrastructure gaps.
Conclusion: AI and digital technologies are transforming OHS through predictive analytics and real-time surveillance. To fully leverage these benefits, future efforts must focus on addressing data quality issues, establishing robust ethical frameworks, and developing advanced infrastructure. Further research is essential for the practical implementation of AI in a variety of work environments.
