Early-Stage Depression Detector Using IoMT

Psychological stress influences the activity of normal bodily physiological function of an individual. Stress has its own consequences in every different individual. Early-Stage Stress Detector using IoMT is proposed to monitor stress levels through monitoring Cortisol level, temperature, movement rate and perspiration rate at the time of stress. This device is developed using the IOMT- Internet of Medical Things. For implementing this device, we use Mamdani type fuzzy logic controller, which is a neural network approach with more than 200 rules as the model. In order to real time monitor the stress levels of a person, the validated data is sent and stored in the cloud (Thingspeak platform), thereby periodic monitoring and health risks are reduced at the earliest. Early-Stage Depression Detector classifies the stress into three types, Low stress, Normal stress and High stress. This classification is done according to the observance of the changes in the physical state of the body. This idea is mainly focused on the mental health monitoring of students, by their parent or guardian. Even though this method is real time, it consumes low energy. The proposed system is at high advantage of accuracy, low systematic complications and cost affordable.