Multimodal Depression Detection Using AI-Based Behavioural Analysis

Abstract

Depression is a major global mental health disorder that often remains undiagnosed due to the subjective nature of traditional clinical assessments and limited access to mental healthcare services. Recent advancements in artificial intelligence have enabled the development of automated systems that analyze behavioral signals for early depression detection. This paper presents a multimodal depression detection framework using AI-based behavioral analysis by integrating textual, acoustic, and visual modalities. Deep learning architectures such as Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM) networks, and transformer-based models are employed to extract discriminative features from each modality. A fusion strategy is applied to combine complementary information and improve prediction accuracy. The proposed approach is evaluated using benchmark datasets such as DAIC-WOZ and AVEC, demonstrating that multimodal fusion significantly outperforms unimodal systems. This work highlights the effectiveness of AI-driven multimodal analysis as a reliable, non-invasive tool to support early diagnosis and monitoring of depression.

Introduction

The text discusses depression as a widespread mental health disorder that significantly impacts individuals’ quality of life, productivity, and well-being. Traditional diagnostic methods—such as clinical interviews and self-report questionnaires—are subjective, time-consuming, and often limited by social stigma and accessibility issues. Behavioral cues in text, speech, and facial expressions provide objective indicators of depression, but unimodal approaches fail to capture its multifaceted nature.

The literature review highlights various automated detection methods: text-based linguistic and sentiment analysis, acoustic feature analysis, CNN-based visual analysis, sequential temporal modeling (LSTM, BiLSTM, 3D-CNN), transformer-based attention models, multimodal fusion techniques, and social media behavioral analysis. Multimodal approaches integrating text, speech, and visual data outperform unimodal systems by providing a more reliable, comprehensive assessment. Challenges include data synchronization, missing modalities, computational complexity, and ethical concerns.

The proposed system implements a multimodal depression detection framework that analyzes textual, acoustic, and visual data. Each modality is processed using specialized deep learning models (transformers for text, LSTM/BiLSTM for speech, CNNs with temporal layers for visuals). Features are fused using a late fusion strategy with attention mechanisms to dynamically weight modality contributions, enhancing robustness and classification accuracy. The system is modular, non-invasive, and scalable for telemedicine and mental health platforms. Experimental validation is conducted on benchmark datasets (DAIC-WOZ, AVEC) with preprocessing, augmentation, and cross-validation to ensure reliable performance.

Conclusion

This paper presents a comprehensive AI-based multimodal depression detection framework that integrates textual, acoustic, and visual behavioral analysis. By leveraging advanced deep learning architectures and multimodal fusion strategies, the proposed system addresses key limitations of traditional diagnostic methods and unimodal approaches. The results demonstrate improved accuracy, robustness, and applicability for early depression detection. Future work will focus on explainable AI, privacy-preserving learning, and real-world clinical validation to further enhance system reliability and ethical deployment.

References

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Copyright

Copyright © 2025 Anagha A B, T P Devaprayag, Mahalakshmi V S, Maneeshkumar G, Binu Balakrishnan. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.