The next generation of fatigue prediction models: evaluating current trends in biomathematical modelling

BACKGROUND: Due to the negative effects of occupational fatigue on health, absenteeism, and economic cost it is essential to control and manage its risk factors effectively. OBJECTIVE: This study seeks to draw researchers’ attention to the research requirements concerning occupational fatigue. METHODS: The study briefly explores the consequences of occupational fatigue and discusses tools for its assessment. It then addresses the challenge of integrating risk factors and identifying efficient interventions. Lastly, it emphasizes the importance of addressing occupational fatigue related to new technologies. RESULTS: Wearable sensors, biomarkers in biological samples, and image processing are valuable tools for accurately assessing occupational fatigue. Artificial intelligence (AI) models can integrate multiple risk factors; while economic evaluations can help assess the effectiveness of control measures. Employers and researchers should be prepared to manage and monitor occupational fatigue resulting from interactions with new technologies. CONCLUSIONS: This commentary highlights the research gap in the field of occupational fatigue to better manage this phenomenon in today’s evolving world.

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The next generation of fatigue prediction models: evaluating current trends in biomathematical modelling

Abstract: The next generation of fatigue prediction models: Evaluating current trends in biomathematical modelling. Theoretical Issues in Ergonomics Science, 0(0), 1-23.

Cited by 3 publications

References 93 publications

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