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This is an extended abstract for Research Demo Session based on our published article [1]. One of the major vulnerabilities of the Internet of Medical Things (IoMT) devices is identity spoofing. As a solution, a device authentication protocol is presented in this paper which authenticates the devices in the network without storing the information in the memory.Physical Unclonable Functions (PUFs) are used for giving a unique identity to each device present in the network and for being authenticated when transmitting the data to the serve 

Most of the health monitoring applications for response plans are used to alert or notify the users in case of emergency situations. Response plans help in overcoming an emergency scenario in case of a disaster. On several occasions, the person of interest receives medical attention, once there is an on-set of the medical condition. With current smart healthcare facilities, where there are advantages of monitoring one's health on a daily basis, a person does not need to wait to be critically ill or meet with a disaster in order to receive necessary medical services. Leveraging the advantages of smart healthcare architectures in this research, we propose a smart rapid medical response plan, which monitors the physiological signs of people in a community and gives regular feedback or alerts the hospitals accordingly. The proposed framework provides feedback on different scales by ensuring the well-being of the individuals and alerting them to be cautious towards potential health issues. The routing of these sensor networks based on the emergency level is demonstrated using an open-source tool, CupCarbon. The proposed framework was simulated using the ZigBee radio standard and the overall simulation time for 40 nodes was 95 seconds. 

It is imperative to find the most accurate way to detect falls in elders to help mitigate the disastrous effects of such unfortunate injuries. In order to mitigate fall related accidents, we propose the Good-Eye System, an Internet of Things (IoT) enabled Edge Level Device which works when there is an orientation change detected by camera, and monitors physiological signal parameters. If the observed change is greater than the set threshold, the user is notified with information regarding a prediction of fall or a detection of fall, using LED lights. The Good-Eye System has a remote wall attached camera to monitor continuously the subject as long as the person is in a room along with a camera attached to a wearable to increase the accuracy of the model. The observed accuracy of the Good-Eye System as a whole is approximately 95%.