Research on depression and emergency detection model using smartphone sensors

Abstract

Due to the deepening of COVID-19, high-intensity social distancing has been prolonged and many social problems have been cured. In particular, physical and psychological isolation occurred due to the non-face-to-face system and a lot of damage occurred. The various social problems caused by Corona acted as severe stress for all those affected by Corona 19, and eventually acted as a factor threatening mental health such as depression. While the number of people suffering from mental illness is increasing, the actual use of mental health services is low. Therefore, it is necessary to establish a system for people suffering from mental health problems. Therefore, in this study, depression detection and emergency detection models were constructed based on sensor information using smartphones from depressed subjects and general subjects. For the detection of depression and emergencies, VAE, DAGMM, ECOD, COPOD, and LGBM algorithms were used. As a result of the study, the depression detection model had an F1 score of 0.93 and the emergency situation detection model had an F1 score of 0.99. direction.