A Web-Based Longitudinal Mental Health Monitoring System

Current clinical assessments of depression disorder are heavily relied on the questionnaire tables on patients' daily behavior, sleeping, and mood status of the past two weeks. However, the information obtained through the patient's review of the past two weeks' experience is neither timely nor objective. Moreover, while patients have medicine at home, doctors lose the way of monitoring and intervening them on time. In this paper, we propose and implement a web-based longitudinal mental health monitoring system. On the user end, the patients can report their daily information through ecological momentary assessment (EMA), share their emotions in speech or face video, test their depression severity through the PHQ-9 questionnaire table or face videos recorded while going through a semi-structured interview, and check their recent history of activity, sleeping, emotion log, and depression severity etc. The server end implements emotion recognition and depression estimation on the pre-trained deep learning models. On the doctor end, the doctor can manage the information of all the patients under his(her) supervision, monitor their recent status, and edit their depression severity after clinical diagnosis.