Patient Behaviour Analysis and Social Health Predictions through IoMT

In the age of ubiquitous computing, people around the globe are benefiting from a more contemporary and intelligent way of living with the help of IoMT (Internet of Medical Things). The Internet of Medical Things (IoMT) and e-healthcare are envisioned to make it simple for individuals to access high-quality home healthcare. Smart healthcare is a convergence of improvements in IoMT, smart wearables, and communication technologies. Using communication technology and software, IoMT unites smart trackers, patients, caretakers, and healthcare providers on a unified platform. The healthcare system has undergone a transformation attributable to IoMT, and it possesses much more potential. By effectively integrating IoT sensors and communication technologies, IoMT has considerably lowered the cost and energy consumption of e-health care. The importance of social and mental health cannot be overstated. According to a global poll, 33 percent of adults worldwide are alone. Unfortunately, this number is swelling every day. In this paper, we present a unique model to monitor a person's social interactions using particular metrics and data collected by wearable tech and sent to the cloud through IoMT. The sigmoid function is used to evaluate the data, and a particular threshold value is used to determine a person's appropriate social behavior. The proposed work is one of a kind.