ABSTRACT
Managing sensory data captured in leveraging is a challenge, especially during a pandemic when trying to capture the psychological, emotional, and physiology standards. The advanced technology of edge computing and IIoMT together help to reach promising outcome results from the home environment using psychological feelings and somatic health equivalent data. The basic application of Deep Learning leads to the asset-constraint of edge computing, which provides a way to move the data that is collected from IIoMT devices to various locations. All kinds of data related to health can exist in a particular place of user edge while assuring the security, privacy, and low latency of the inference system. In this article, an Internet of Medical system is developed that uses Deep Learning to detect risky types of health-related symptoms and generates reports and alerts for pandemic and epidemic situations, which helps in decision-making support. In these pandemic and epidemic situations, a lot of applications have been identified and implemented with their descriptions for the upcoming support for the real-time trials. We have developed smart applications in edge computing manuals. The overall output clearly allows us to view the fixed smart systems during the pandemic with the Smart Health Management system (SHMs). © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.