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Anxiety disorders affect more than 18 percent of the population and is the most common mental illnesses in the US. There is a great demand to address this emerging epidemic with tools to differentiate and diagnose such disorders, and to create awareness especially in places like NorthEast Texas which is home to 1.5 million people with 58 percent of them living in rural areas. The goal of the proposed device is to diagnose as many anxiety disorders as possible, in real-time using the diagnosing wearable framework, SolicitudeSavvy, which uses technology such as the Internet of Things (IoT), a network of interconnected devices, to accomplish such a task. The proposed IoT-based device has two components: a custom-built wearable necklace that contains sensors to collect data about the user as they go about their day and a low-cost portable system that monitors Electrooculography (EoG) signals using a camera. The partial necklace attaches to the shirt and opens halfway around the wearer's neck and the EoG can be attached to any eyewear. The device monitors the user throughout the day, and even as they go to bed at night. This information is accumulated in the IoT cloud and analyzed to see exactly what type of disorder(s) the patient may suffer from. The authorized personnel i.e. doctor or therapist can use this pattern to find a treatment that best suits them and is most likely to resolve their affliction. 

Overindulgence of harmful substances such as drugs or alcohol, called substance abuse, can directly affect a person's health and their day-to-day activities. The younger population become more vulnerable to such use of psychoactive substances due to lack of awareness of the long-term hazardous effects these substances can have on their health. Additionally, these individuals tend to develop severe mental disorders as they grow older. With the boom of Internet of Things (IoT), the use of wearable sensors such as smartwatches and smartphones has tremendously increased. These wearables help in monitoring a person's physiological signal and keep them informed of one's health. In this research, we propose an edge-intelligent IoT-based wearable that can assist in substance-abuse detection by monitoring their physiological signals on daily basis. The proposed system helps in monitoring the substance abuse and craving of the individual and help the healthcare provider to start an early intervention as required. The proposed system is validated using a custom-built wearable, i-SAD, which was developed as a dedicated substance abuse wearable using commercially available off-the-shelf components. The proposed wearable design was validated using medical quality wearable and yielded a correlation of 0.89 for accelerometer values and 0.92 for average heart rate values. 

Unlike the younger population that uses wearables such as smartwatches for monitoring health on a daily basis, elderly people need assistance in the use of technology and interpreting the data obtained through these smart connected frameworks. The current monitoring systems are primarily designed to monitor the physiological signals on a daily basis. The aim of this proposed research, Easy-Assist, is to help older people to maintain their emotional well-being. This research is focused on developing a wearable affective framework, which can help in detecting the emotions of the user in addition to monitoring their physiological signals. The proposed framework can be used in an automated assisted living environment, where the user's emotional state can be balanced using a haptic-based emotional elicitation system after the user's emotion is recognized, detected and interpreted in real-time. The proposed framework is validated using a fall detection algorithm deployed in a custom-built watch wearable, built using off-the-shelf components and an emotion detection framework built using a single board computer. A dataset of 21700 samples acquired using the proposed framework yielded a maximum efficiency of 97.25%, 96 %, and 94 %, in classifying the state and emotion classes into Alert, Active and Normal classes respectively, using multi-class SVM model. The overall latency of the proposed research was in few orders of milli-seconds.