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Comparative Analysis of Visual Motion and Multimodal Strategies in Depression Recognition

by Hemangi Kacha, Drashti Bhikadiya, Abhijeetsinh Jadeja, Kamini Sharma, Jayashri Patil
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 187 - Number 58
Year of Publication: 2025
Authors: Hemangi Kacha, Drashti Bhikadiya, Abhijeetsinh Jadeja, Kamini Sharma, Jayashri Patil
10.5120/ijca2025925983

Hemangi Kacha, Drashti Bhikadiya, Abhijeetsinh Jadeja, Kamini Sharma, Jayashri Patil . Comparative Analysis of Visual Motion and Multimodal Strategies in Depression Recognition. International Journal of Computer Applications. 187, 58 ( Nov 2025), 58-64. DOI=10.5120/ijca2025925983

@article{ 10.5120/ijca2025925983,
author = { Hemangi Kacha, Drashti Bhikadiya, Abhijeetsinh Jadeja, Kamini Sharma, Jayashri Patil },
title = { Comparative Analysis of Visual Motion and Multimodal Strategies in Depression Recognition },
journal = { International Journal of Computer Applications },
issue_date = { Nov 2025 },
volume = { 187 },
number = { 58 },
month = { Nov },
year = { 2025 },
issn = { 0975-8887 },
pages = { 58-64 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume187/number58/comparative-analysis-of-visual-motion-and-multimodal-strategies-in-depression-recognition/ },
doi = { 10.5120/ijca2025925983 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2025-11-18T21:11:20.808599+05:30
%A Hemangi Kacha
%A Drashti Bhikadiya
%A Abhijeetsinh Jadeja
%A Kamini Sharma
%A Jayashri Patil
%T Comparative Analysis of Visual Motion and Multimodal Strategies in Depression Recognition
%J International Journal of Computer Applications
%@ 0975-8887
%V 187
%N 58
%P 58-64
%D 2025
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The use of computer vision and artificial intelligence (AI) technologies gives people the chance to study automated systems designed to evaluate visual and motion-based signals of depressive behavior. This review assesses the current state of research on facial landmark tracking, head pose estimation, and multimodal feature integration.Motion-based methodologies in the form of kineme modeling, rotation-invariant geometric frameworks, and interpretable motion dynamics explore relationships between motor behavior and depression. The use of visual techniques that combine facial landmarks, temporal geography, and attention-driven deep networks provides high prediction accuracy, although performance is still affected by lighting, pose, and culture. Multimodal systems that use combinations of facial, verbal, and textual streams of data add explanatory power to the diagnosis, but also issues surrounding explainability and temporal imbalance. Together, the studies highlight the diverse range of methodologies that are being employed to develop automated systems to identify depression in individuals.

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Index Terms

Computer Science
Information Sciences

Keywords

Depression Depression Detection Deep Learning Emotion Facial Detection Motion-Visual-Multimodal Approaches

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