Design and Development of Wearable Devices for Panic Attack Detection

The purpose of this thesis is to design and develop a system that can identify panic attacks through the analysis of vital signs such as heart rate, blood’s oxygen level, and body temperature. In everyday life, we can observe an increase in anxiety and constant panic due to life pressure, financial crises, and environmental factors. In addition, each panic attack is an incredibly stressful and overwhelming situation, in some cases accompanied by loss of control and seizures, eventually affecting a person's life, including anxiety, isolation, and an impact on daily activities. It is important to learn how to control panic attacks, and there are some strategies to manage this stress, such as breathing exercises or meditation. However, at this point, we would like to explore the role of smart technologies and the Internet of Things (IoT) in managing panic attacks. The use of wearable devices with sensors that monitor physiological factors such as heart rate, oxygen saturation, body heat, sleep, and breathing patterns can help detect panic attacks even when the person is alone and inform healthcare by asking for help. In the second step, these wearable devices can collect data, such as how often a seizure occurs and under what circumstances. Storing this information in databases and analyzing it using patterns and machine learning will be helpful in anticipating and predicting upcoming seizures and ensuring a person's safety and well-being.