An integrated approach for mental health assessment using emotion analysis and scales

Abstract

Depression is a prominent cause of mental illness, which could primarily increase early death. It is possible that this is the root of suicidal ideation, and it causes severe impairment in daily life. By detecting human face traits, artificial intelligence (AI) has cleared the road for predicting human emotions. This predictive technique will be used to conduct a preliminary assessment of depression. Prediction is accomplished using a mixture of four modules namely Facial Emotion Recognition (FER), Scales Questionnaire, Speech Emotion Recognition (SER), and Doctor Chat. FER2013 dataset is used for the FER module, while for speech-based recognition, RAVDESS, TESS, SAVEE, and CREMA-D are collectively used. To improve the accuracy of the FER, the people in the given image will be fed into a Face API created with TensorFlow JS, which will eventually be given to the proposed model that will recognize human faces in the image. For SER, a python library known as Librosa is used for extracting audio features and it will be fed to the proposed model. The scales module of the app has questionnaires that can be answered, and the result can be generated based on the scores obtained using established scales used in modern psychology such as the HAM-D, YMRS etc., Though deep learning can predict emotions, the user may choose to speak with a real doctor about the issues to clear up any doubts. The application has a Doctor Chat module, which is essentially a chat bot for interacting with a doctor. Using this module, the users can talk, exchange files, and have their questions answered. The accuracy of FER is 91% whereas for SER, it is 82% on the test sets. The proposed approach produces the highest accuracy for the benchmark dataset. These four modules will work together to produce a homogenous depression report.