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http://dx.doi.org/10.36498/kbigdt.2022.7.1.29

Mental Healthcare Digital Twin Technology for Risk Prediction and Management  

SeMo Yang (가천대학교 컴퓨터공학과)
KangYoon Lee (가천대학교 컴퓨터공학과)
Publication Information
The Journal of Bigdata / v.7, no.1, 2022 , pp. 29-36 More about this Journal
Abstract
The prevalence of stress and depression among emotional workers is increasing due to the rapid increase in emotional labor and service workers. However, the current mental health management of emotional workers is difficult to consider the emotional response at the time of stress situations, and the existing mental health management is limited because the individual's base state is not reflected. In this study, we present mental healthcare digital twin solution technology, a personalized stress risk management solution. For mental health risk management due to emotional labor, a solution simulation is performed to accurately predict stress risk through synchronization/modeling of dynamic objects in virtual space by extracting individual stress risk factors such as emotional/physical response and environment into various modalities. It provides a mental healthcare digital twin solution for predicting personalized mental health risks that can be configured with modalities and objects tailored to the environment of emotional workers and improved according to user feedback.
Keywords
Emotional Worker; Mental Healthcare; Digital Twin; Multi Modality; Stress Prediction; Intervention;
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Times Cited By KSCI : 3  (Citation Analysis)
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