A System and Method to Detect Anxiety Using Detect, Alert, and Distract Anxiety (DADA) Model and Algorithm

This dissertation delineates research in PTSD, and other anxieties in the military, firefighters, medical caregivers, and law enforcement domains. It is a comprehensive review of PTSD symptoms, training, treatments, and psychological, physiological, biological, neurological, diet, sleep, and environmental impact on people suffering from anxiety. It presents a new way to detect anxiety and control it without medicinal drugs, treatments, or training. It empowers people to control their anxiety on their own and improve their quality of life. It proposes a Detect, Alert, Distract Anxiety (DADA) model, which detects user\u27s anxiety, alerts the user of their symptoms in real-time, and encourages the use of distraction strategies to help distract user\u27s negative thoughts and emotions. The engine of the DADA model is the Anxiety Detection (AD) algorithm, which facilitates continuous detection and monitoring of anxiety symptoms. It presents a prototype engineering solution that facilitates real-time monitoring and feedback of anxiety symptoms automatically. It has the potential to save people from committing suicide by alerting them every time they are experiencing anxiety to distract their negative thoughts and emotions. There are a total of three studies conducted in support of this dissertation. The study one encourages the need for creating an engineering solution to help combat anxiety by showing that taking a healthy diet, having enough sleep, and consuming less harmful chemicals found in food and environment does not equate to an anxiety-free life. Study two collects data on brainwaves and R-R interval from people suffering from speech anxiety to generate an anxiety detection (AD) algorithm. Study three promises the usefulness of the DADA model in potentially reducing anxiety and the effectiveness of the AD algorithm