Open AccessAutomatic Depression Level Detection through Visual Input
Author(s) -
Prof. Poonam Hadke,
Ghanshyam Chaudhari,
Sagar Dhadge,
Deep Kukkadgaonkar
Publication year - 2022
Publication title -
international journal of advanced research in science, communication and technology
Language(s) - English
Resource type - Journals
ISSN - 2581-9429
DOI - 10.48175/ijarsct-2663
Subject(s) - beck depression inventory , depression (economics) , mood , psychology , computer science , support vector machine , process (computing) , field (mathematics) , mental health , visual field , artificial intelligence , clinical psychology , psychiatry , mathematics , anxiety , neuroscience , pure mathematics , economics , macroeconomics , operating system
Depression is the most comprehensive mood ailment that has a notable influence on mental health as well as hindrances in daily life. Machine learning models have contributed to the field of emotion detection in all areas including audio, visual and internet-based text data. The idea directs at developing a machine learning based model utilising images and video as an input, to analyze the level of depression among users. Based on the analysed features the individual will be classified into either of the following depression categories: Minimal, Mild, Moderate, Severe. In the process of depression level detection, the two crucial components are video input and the Beck Depression Inventory II. The solution generates as a result of the correlation between emotion vector and inventory vector represented using visual graphics.