Deep learning for depression recognition with audiovisual cues: A review

L He, M Niu, P Tiwari, P Marttinen, R Su, J Jiang… - Information …, 2022‏ - Elsevier
With the acceleration of the pace of work and life, people are facing more and more
pressure, which increases the probability of suffering from depression. However, many …

Automatic assessment of depression based on visual cues: A systematic review

A Pampouchidou, PG Simos, K Marias… - IEEE Transactions …, 2017‏ - ieeexplore.ieee.org
Automatic depression assessment based on visual cues is a rapidly growing research
domain. The present exhaustive review of existing approaches as reported in over sixty …

Challenges for artificial intelligence in recognizing mental disorders

WJ Yan, QN Ruan, K Jiang - Diagnostics, 2022‏ - mdpi.com
Artificial Intelligence (AI) appears to be making important advances in the prediction and
diagnosis of mental disorders. Researchers have used visual, acoustic, verbal, and …

Toward a social psychophysics of face communication

RE Jack, PG Schyns - Annual review of psychology, 2017‏ - annualreviews.org
As a highly social species, humans are equipped with a powerful tool for social
communication—the face. Although seemingly simple, the human face can elicit multiple …

Machine learning based approaches for clinical and non-clinical depression recognition and depression relapse prediction using audiovisual and EEG modalities: A …

S Yasin, A Othmani, I Raza, SA Hussain - Computers in Biology and …, 2023‏ - Elsevier
Mental disorders are rapidly increasing each year and have become a major challenge
affecting the social and financial well-being of individuals. There is a need for phenotypic …

Deep neural networks for depression recognition based on 2d and 3d facial expressions under emotional stimulus tasks

W Guo, H Yang, Z Liu, Y Xu, B Hu - Frontiers in neuroscience, 2021‏ - frontiersin.org
The proportion of individuals with depression has rapidly increased along with the growth of
the global population. Depression has been the currently most prevalent mental health …

[HTML][HTML] Fusing features of speech for depression classification based on higher-order spectral analysis

X Miao, Y Li, M Wen, Y Liu, IN Julian, H Guo - Speech Communication, 2022‏ - Elsevier
Approximately 300 million people worldwide suffer from depression, and more than 60% of
psychiatric patients do not have access to mental health services due to the shortage of …

Automatic identification of depression using facial images with deep convolutional neural network

X Kong, Y Yao, C Wang, Y Wang… - … Medical Journal of …, 2022‏ - pmc.ncbi.nlm.nih.gov
Background Depression is a common disease worldwide, with about 280 million people
having depression. The unique facial features of depression provide a basis for automatic …

A Comprehensive Analysis of Speech Depression Recognition Systems

A Hassan, S Bernadin - SoutheastCon 2024, 2024‏ - ieeexplore.ieee.org
Being the third most common cause of disability globally, clinical depression is a serious
global health concern that is characterized by melancholy, loneliness, and low self-esteem …

Enacting 'more-than-human'care: Clients' and counsellors' views on the multiple affordances of chatbots in alcohol and other drug counselling

A Barnett, M Savic, K Pienaar, A Carter… - International Journal of …, 2021‏ - Elsevier
Forms of artificial intelligence (AI), such as chatbots that provide automated online
counselling, promise to revolutionise alcohol and other drug treatment. Although the …