Patient Behaviour Analysis and Social Health Predictions through IoMT
Prerna Ajmani, Vandana Sharma, Prithi Samuel, K. Somasundaram, V. Vidhya
Abstract
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.