Capturing psychological, emotional, and physiological states, especially during a pandemic, and leveraging the captured sensory data within the pandemic management ecosystem is challenging. Recent advancements for the Internet of Medical Things (IoMT) have shown promising results from collecting diversified types of such emotional and physical health-related data from the home environment. State-of-the-art deep learning (DL) applications can run in a resource-constrained edge environment, which allows data from IoMT devices to be processed locally at the edge, and performs inferencing related to in-home health. This allows health data to remain in the vicinity of the user edge while ensuring the privacy, security, and low latency of the inferencing system. In this article, we develop an edge IoMT system that uses DL to detect diversified types of health-related COVID-19 symptoms and generates reports and alerts that can be used for medical decision support. Several COVID-19 applications have been developed, tested, and deployed to support clinical trials. We present the design of the framework, a description of our implemented system, and the accuracy results. The test results show the suitability of the system for in-home health management during a pandemic.
Keywords: Affective computing; Internet of Medical Things (IoMT); deep learning (DL); emotion analysis.