IoT Based Framework Design for Automated Human Emotion Recognition

The most important aspect of a human being is their mental health, yet it is also the biggest difficulty for society to maintain it in such a fast-paced world. This mostly leads to a state of mental stress, sadness, and anxiety. It also leads to the root cause of several severe health problems such as high blood pressure, heart attacks, and even abrupt fatalities, as well as self-destruction. Normally, counseling, questioning, and close observation of a person can identify all these issues. In this paper the focus is placed on physical changes such as the capacitance of a stressed-out person's sweat, the heart rate of a depressed individual, or the facial expressions of a person. Several studies focus on real-time emotion recognition using physiological data gathered from ubiquitous sensors, although most of the existing research in this area primarily relies on facial data. The framework integrates most of the work, incorporating sensors that are cost-efficient and simple in arrangement. The approach utilizes smart phone and wearable technology, implementing a real-time emotion identification system. Subsequently, alerts are sent to medical professionals and families. This model is proposed based on the concept of IoMT (Internet of Medical Things) and with the assistance of Machine Learning classification techniques. This work not only recognizes a person's emotions but also, with the database maintained, can predict if a person can be prone to depression in the long term.