International Journal For Multidisciplinary Research

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Video Based Depression Detection And Analysis

Author(s) Shivani Karhale, Komal Chunge, Vaishnavi Kale, Afiya Papde
Country India
Abstract Depression is one of the most serious and frequent diagnosed mental disorders. It affects not only the sufferers but also their families, friends and even the society in general. With rapid advancements in Artificial Intelligence and Machine Learning, there have been some new developments that aim to predict the strength of depression in an individual by analyzing certain parameters extracted from their video sample. This paper discusses some of the new and most worthy of note researches in the domain of depression analysis and balance their different frameworks like CNNs, RNN, etc. and algorithms used. The strength of depression is identified by using the Beck Depression Inventory-II (BDI-II) that range from 0 to 63. The performance of all these models is compared based on the structure (architecture and algorithms) assume and based on the MAE & RMSE scores obtained after testing them on AVEC 2013 & AVEC 2014 datasets. Apart from the comparative analysis, this paper also discusses a couple of approaches that study some unconventional parameters that scientific analysis in the task of depression detection and prediction from video data.
Keywords Depression analysis, CNN, BDI-II, AVEC, Comparative analysis.
Field Engineering
Published In Volume 5, Issue 5, September-October 2023
Published On 2023-10-28
Cite This Video Based Depression Detection And Analysis - Shivani Karhale, Komal Chunge, Vaishnavi Kale, Afiya Papde - IJFMR Volume 5, Issue 5, September-October 2023. DOI 10.36948/ijfmr.2023.v05i05.8023
DOI https://doi.org/10.36948/ijfmr.2023.v05i05.8023
Short DOI https://doi.org/gs3hgm
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E-ISSN 2582-2160
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