Machine learning for multimodal mental health detection: a systematic review of passive sensing approaches

LS Khoo, MK Lim, CY Chong, R McNaney - Sensors, 2024‏ - mdpi.com
As mental health (MH) disorders become increasingly prevalent, their multifaceted
symptoms and comorbidities with other conditions introduce complexity to diagnosis, posing …

Automatic depression recognition by intelligent speech signal processing: A systematic survey

P Wu, R Wang, H Lin, F Zhang, J Tu… - CAAI Transactions on …, 2023‏ - Wiley Online Library
Depression has become one of the most common mental illnesses in the world. For better
prediction and diagnosis, methods of automatic depression recognition based on speech …

Self-trained deep ordinal regression for end-to-end video anomaly detection

G Pang, C Yan, C Shen, A Hengel… - Proceedings of the …, 2020‏ - openaccess.thecvf.com
Video anomaly detection is of critical practical importance to a variety of real applications
because it allows human attention to be focused on events that are likely to be of interest, in …

Depression detection from social networks data based on machine learning and deep learning techniques: An interrogative survey

KM Hasib, MR Islam, S Sakib, MA Akbar… - IEEE Transactions …, 2023‏ - ieeexplore.ieee.org
Users can interact with one another through social networks (SNs) by exchanging
information, delivering comments, finding new information, and engaging in discussions that …

[PDF][PDF] Detecting Depression with Audio/Text Sequence Modeling of Interviews.

T Al Hanai, MM Ghassemi, JR Glass - Interspeech, 2018‏ - isca-archive.org
Medical professionals diagnose depression by interpreting the responses of individuals to a
variety of questions, probing lifestyle changes and ongoing thoughts. Like professionals, an …

A multimodal fusion model with multi-level attention mechanism for depression detection

M Fang, S Peng, Y Liang, CC Hung, S Liu - Biomedical Signal Processing …, 2023‏ - Elsevier
Depression is a common mental illness that affects the physical and mental health of
hundreds of millions of people around the world. Therefore, designing an efficient and …

EEG based Major Depressive disorder and Bipolar disorder detection using Neural Networks: A review

S Yasin, SA Hussain, S Aslan, I Raza… - Computer Methods and …, 2021‏ - Elsevier
Mental disorders represent critical public health challenges as they are leading contributors
to the global burden of disease and intensely influence social and financial welfare of …

[HTML][HTML] Automated depression analysis using convolutional neural networks from speech

L He, C Cao - Journal of biomedical informatics, 2018‏ - Elsevier
To help clinicians to efficiently diagnose the severity of a person's depression, the affective
computing community and the artificial intelligence field have shown a growing interest in …

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 …

Artificial intelligent system for automatic depression level analysis through visual and vocal expressions

A Jan, H Meng, YFBA Gaus… - IEEE Transactions on …, 2017‏ - ieeexplore.ieee.org
A human being's cognitive system can be simulated by artificial intelligent systems.
Machines and robots equipped with cognitive capability can automatically recognize a …