Artificial Intelligent System for Automatic Depression Level Analysis Through Visual and Vocal Expressions
Open accessAbstract
A human being's cognitive system can be simulated by artificial intelligent systems. Machines and robots equipped with cognitive capability can automatically recognize a humans mental state through their gestures and facial expressions. In this paper, an artificial intelligent system is proposed to monitor depression. It can predict the scales of Beck depression inventory II (BDI-II) from vocal and visual expressions. First, different visual features are extracted from facial expression images. Deep learning method is utilized to extract key visual features from the facial expression frames. Second, spectral low-level descriptors and mel-frequency cepstral coefficients features are extracted from short audio segments to capture the vocal expressions. Third, feature dynamic history histogram (FDHH) is proposed to capture the temporal movement on the feature space. Finally, these FDHH and audio features are fused using regression techniques for the prediction of the BDI-II scales. The proposed method has been tested on the public Audio/Visual Emotion Challenges 2014 dataset as it is tuned to be more focused on the study of depression. The results outperform all the other existing methods on the same dataset.
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Multiple studies have demonstrated the effectiveness of AI-enabled emotion analysis in assessing patients' depressive states and recommending timely intervention, thereby improving mental health care.
“Multiple studies have demonstrated the effectiveness of AI-enabled emotion analysis in assessing patients’ depressive states and recommending timely intervention, thereby improving mental health care [22,26].”
From Attitudes Toward the Adoption of 2 Artificial Intelligence–Enabled Mental Health Tools Among Prospective Psychotherapists: Cross-sectional Study (2023) · cites this paper as [26]
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References 64 · Cited by 219
Research works printed but not linked: 6
- M. F. Valstar et al., "AVEC 2013: The continuous audio/visual emo- tion and depression recognition challenge," in Proc. Int. Conf. ACM Multimedia Audio/Vis. Emotion Challenge Workshop, 2013, pp. 3-10.
- M. Valstar et al., "AVEC 2016: Depression, mood, and emotion recognition workshop and challenge," in Proc. 6th Int. Workshop Audio/Vis. Emotion Challenge (AVEC), Amsterdam, The Netherlands, 2016, pp. 3-10.
- M. F. Valstar et al., "AVEC 2014: 3D dimensional affect and depression recognition challenge," in Proc. Int. Conf. ACM Multimedia Audio/Vis. Emotion Challenge Workshop, Orlando, FL, USA, 2014, pp. 3-10.
- X. Ma, H. Yang, Q. Chen, D. Huang, and Y. Wang, "Depaudionet: An efficient deep model for audio based depression classification," in Proc. 6th Int. Workshop Audio/Vis. Emotion Challenge (AVEC), Amsterdam, The Netherlands, 2016, pp. 35-42.
- J. F. Cohn et al., "Detecting depression from facial actions and vocal prosody," in Proc. Int. Conf. Affective Comput. Intell. Interact. Workshops, Amsterdam, The Netherlands, 2009, pp. 1-7.
- W. T. Freeman and M. Roth, "Orientation histograms for hand ges- ture recognition," in Proc. IEEE Int. Workshop Autom. Face Gesture Recognit., Zürich, Switzerland, 1994, pp. 296-301.
- Authors
- Jan, A., Meng, H., Gaus, Y. F. B. A., & Zhang, F.
- Affiliations
- Brunel University of London (GB)
- Venue
- IEEE Transactions on Cognitive and Developmental Systems
- Published
- 2018-09
- Volume
- 10
- Issue
- 3
- Pages
- 668-680
- ISSN
- 2379-8920 (ISSN-L)
- 2379-8939
- OpenAlex
- W2736814566
- Cited by
- 219
- References
- 64
- Citations by year
- 2026: 20 · 2025: 31 · 2024: 27 · 2023: 42 · 2022: 35 · 2021: 24 · 2020: 18 · 2019: 18 · 2018: 4
- FWCI
- 14.25
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- IEEE Transactions on Cognitive and Developmental Systems (publishedVersion)
- Funders
- School of Engineering and Design
- Thomas Gerald Gray PGR Scholarship
- Royal Society
- National Natural Science Foundation of China (IE160946)
- Majlis Amanah Rakyat
- Topics
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Emotion and Mood Recognition
- Physical Sciences › Computer Science › Signal Processing › Speech and Audio Processing
- Social Sciences › Psychology › Clinical Psychology › Child and Adolescent Psychosocial and Emotional Development
- Keywords
- depression detection, Beck Depression Inventory-II, facial expression analysis, audiovisual integration, Mel-frequency cepstral coefficients, spectral properties, temporal feature modeling, regression prediction
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