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The need for a transformation in providing healthcare has been recognized by organizations and captured in reports. Research into Smart Health using Artificial Intelligence (AI) could help identify the mental health of individuals by analyzing physiological data. The complexity of emotions can make it challenging for an individual to recognize they are coping with mental illness. AI could be used as an objective method in recognizing mental health crisis. This is where smart emotion could help as a Human-in-the-loop system that can reduce the time it takes for an individual to get treatment by identifying mental illness. Early treatment of mental health crises can lead to an overall reduction in damage caused by it. Further, COVID-19 has overwhelmed many healthcare systems, leading malicious actors to target them, highlighting many Cybersecurity issues. AI could aid in addressing Cybersecurity concerns to create a robust and secure Human-in-the-Loop system for mental health problems.

The advent of the Internet of Things (IoT) allows the Cyber-Physical System (CPS) components to communicate with other devices, and to interact with safety-critical systems, posing new research challenges in security, privacy, and reliability. Efficient power measurement in smart IoT devices has become one of the key research topics. In this paper, we design and develop a CPS to detect IoT security threats via behavioral power profiling of a heterogeneous wireless sensor device using a Raspberry Pi and a smartphone. Experimentation and verification have been conducted on a group of smart IoT devices with different test scenarios, including the device in an idle and active state with distributed denial-of-service (DDoS) and a man-in-the-middle (MitM) attack. We propose to use the device power consumption rate to predict and detect a security threat using statistical signal processing and multivariate regression model. The proposed system can detect a potential security threat with an average accuracy of 80% and a device high of 89%.