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Comprehensive Modeling of Occupational Diseases Using Digital Twins: An IoT-Based Approach
Abstract
This chapter focuses on the application and beneficial effects of digital twins in the field of occupational disease prevention and treatment. Taking the IoT approach as an example, this chapter introduces how to use both factory site data and worker physiological data to build digital twins and provide personalized prevention and treatment models through AI-driven data analysis. The practical applicability and advantages of this technology are illustrated in the case study on noise-induced hearing loss (NIHL) in a foundry setting. By integrating environmental noise monitoring with individual health data, digital twins generate precise and personalized representations of the correlation between noise exposure and hearing health. This method improves the health and safety of workers by enhancing the accuracy and efficacy of risk assessments, resulting in more targeted and efficient interventions.
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