E - Therapy Improvement Monitoring Platform for Depression using Facial Emotion Detection of Youth

Abstract

In today's generation depression is the most common mental health disorder which is mainly affecting many people's lifestyles especially the youth generation. There are lot of applications evolved for depression and recent review of depression apps indicates that importance given for video communication in online therapy is low which leads to difficulties while therapist and patients are engaging for therapies. It is also reported that these applications lack proper evidence of improvements which is a significant concern of the therapist and the users in order to monitor the therapy progress. Hence the ultimate aim is to provide an effective application for online depression therapy which has the ability to predict depression of the patient, assist the therapist and the patient to monitor the patient's improvement level and progress on each session using facial expression recognition. The proposed solution consists of depression prediction using feed - forward neural network model and Depression scale to measure the depression level. Calculating the improvement level is based on the depression levels identified and finally Visual represented in a dashboard to monitor depression level improvements for the therapist and the patient.