Emotional Well-being of a Human: A Case Study of Proposing a Digital Twin Solution Integrating IoT Sensors and Cloud Computing

There are many different types of technology that can improve well-being and quality of life through monitoring, comprehension, and continuous feedback. One such technology is the digital twin. Consequently, employing a digital twin as a type of solution can improve an individual's emotional health and quality of life in a surrounding environment. But it will take some time to develop digital twin systems that can make it easier to create and use digital twins because there are many variables that can affect how a digital twin is designed, including the kind of sensor, complementary technologies, the context of the situation being assessed for the person, and the surrounding environment. In this paper, we describe a real-time hybrid sensor fusion strategy for environmental factors, such as temperature, humidity and brightness with respect to an individual's heart rate monitoring using machine learning models such as random forest, linear regression and ridge regression. Further, Explainable AI-SHAP is utilized to enhance the interpretability of the machine learning models, hence guaranteeing the credibility and understanding of the digital twin's predictions in this study. These methods put the idea of real- time feedback to use by introducing a digital twin for emotions of human into practice.