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Atmospheric measurements
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Affective and Content Analysis of Online Depression Communities,
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feature extraction
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Depression.
behavioural sciences computing
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Soft biometrics
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Self-Reported Symptoms of Depression and PTSD Are Associated with
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1603
Acoustics
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MultiSense: Context-Aware Nonverbal Behavior Analysis Framework:
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1706
Affective computing, Computer architecture, Context, Pipelines,
Psychology, Real-time systems, Synchronization, MultiSense,
automatic distress assessment, behavior quantification,
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1706
Feature extraction, Interviews, Pediatrics, Repeaters,
Risk management, Speech, Support vector machines,
Behavior analytics, clinician-patient interaction,
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1703
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1805
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Automated Depression Diagnosis Based on Deep Networks to Encode
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1812
Face recognition, Feature extraction, Histograms, Optical imaging,
Databases, Optical computing, Automated depression diagnosis,
flow dynamics
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Multimodal Depression Detection: Fusion Analysis of Paralinguistic,
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1812
Feature extraction, Speech, Australia, Sensors, Magnetic heads, Mood,
Depression detection, multimodal fusion, speaking behaviour,
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Elsevier DOI
1812
Depression detection, Facial expression, Video processing,
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Mood disorder detection, Convolutional neural network,
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Automatic Depression Analysis Using Dynamic Facial Appearance
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IEEE DOI
1906
Feature extraction, Databases, Histograms, Visualization, Encoding,
Face, Face recognition, Depression, nonverbal behaviors,
Dirichlet process Fisher vector (DPFV)
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AffCom(10), No. 4, October 2019, pp. 445-470.
IEEE DOI
1912
Visualization, Affective computing, Monitoring, Depression,
Image analysis, Machine learning, Depression assessment,
facial image analysis
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Learning systems, Affective computing, Estimation, Depression,
Medical conditions
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Marias, K.,
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Emotion recognition, Fitness function, Genetic programming, EEG,
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IEEE DOI
2006
Speech, Feature extraction, Acoustics, Indexes, Tools,
Predictive models, Signal processing, Bayesian,
two-stage regression
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IEEE DOI
2008
Electroencephalography, Brain modeling, Feature extraction,
Cognition, Medical services, Physiology, Electrodes,
health implications
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Detecting Unipolar and Bipolar Depressive Disorders from Elicited
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AffCom(11), No. 3, July 2020, pp. 393-404.
IEEE DOI
2008
Mood, Databases, Speech, Videos, Emotion recognition, Mental disorders,
Feature extraction, Mood disorder, speech emotion recognition,
latent affective structure model
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Zhou, X.,
Jin, K.,
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Visually Interpretable Representation Learning for Depression
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IEEE DOI
2008
Visualization, Feature extraction, Videos, Face recognition, Face,
Computer architecture, Image recognition, Depression recognition,
depression activation map
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2103
Visualization, Estimation, Support vector machines,
Feature extraction, Histograms, Analytical models, Neural networks,
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Video-Based Depression Level Analysis by Encoding Deep Spatiotemporal
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2103
Face, Visualization, Spatiotemporal phenomena,
Recurrent neural networks, Feature extraction,
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Depression recognition, Facial representation,
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Exploring Macroscopic and Microscopic Fluctuations of Elicited Facial
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AffCom(12), No. 4, October 2021, pp. 989-1001.
IEEE DOI
2112
Face recognition, Microscopy, Mood, Depression, Feature extraction,
Data collection, Motion control, Action unit, facial expression,
wavelet decomposition
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Zhang, Z.X.[Zi-Xing],
Mascolo, C.[Cecilia],
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Schuller, B.W.[Björn W.],
Deep Learning for Mobile Mental Health:
Challenges and recent advances,
SPMag(38), No. 6, November 2021, pp. 96-105.
IEEE DOI
2112
Deep learning, Data privacy, Wearable computers, Collaboration,
Mental health, Signal processing, Smart phones
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Budhi, G.S.[Gregorious Satia],
Dhakal, S.[Sandeep],
Combining Sentiment Lexicons and Content-Based Features for
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IEEE DOI
2112
Sentiment analysis, Social networking (online), Depression,
Feature extraction, Boosting, Intelligent systems, Standards
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Wang, G.[Gang],
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An Improved Empirical Mode Decomposition of Electroencephalogram
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AffCom(13), No. 1, January 2022, pp. 262-271.
IEEE DOI
2203
Electroencephalography, Feature extraction, Depression, Hospitals,
Physiology, Databases, Time-frequency analysis, Depression,
feature extraction
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Dey, N.[Nilanjan],
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DOI Link
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Investigation of Speech Landmark Patterns for Depression Detection,
AffCom(13), No. 2, April 2022, pp. 666-679.
IEEE DOI
2206
Feature extraction, Speech processing, Acoustics, Production,
Speech recognition, Sociology, Depression classification,
naturalistic environments
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Uddin, M.A.[Md Azher],
Joolee, J.B.[Joolekha Bibi],
Lee, Y.K.[Young-Koo],
Depression Level Prediction Using Deep Spatiotemporal Features and
Multilayer Bi-LTSM,
AffCom(13), No. 2, April 2022, pp. 864-870.
IEEE DOI
2206
Depression, Feature extraction, Dynamics, Spatiotemporal phenomena,
Histograms, Nonhomogeneous media, Optical imaging, Depression,
temporal median pooling
BibRef
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Jaiswal, S.[Shashank],
Shen, L.L.[Lin-Lin],
Valstar, M.[Michel],
Spectral Representation of Behaviour Primitives for Depression
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AffCom(13), No. 2, April 2022, pp. 829-844.
IEEE DOI
2206
Depression, Videos, Task analysis, Interviews, Feature extraction,
Magnetic heads, Neural networks, Automatic depression analysis,
convolution neural networks
BibRef
Zhang, X.W.[Xiao-Wei],
Pan, J.[Jing],
Shen, J.[Jian],
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Li, J.L.[Jun-Lei],
Lu, D.W.[Da-Wei],
Wu, M.X.[Man-Xi],
Hu, B.[Bin],
Fusing of Electroencephalogram and Eye Movement With Group Sparse
Canonical Correlation Analysis for Anxiety Detection,
AffCom(13), No. 2, April 2022, pp. 958-971.
IEEE DOI
2206
Electroencephalography, Feature extraction, Correlation, Scalp,
Visualization, Support vector machines, Psychology,
eye movement
BibRef
Lu, H.F.[Hai-Feng],
Xu, S.H.[Shi-Hao],
Hu, X.[Xiping],
Ngai, E.[Edith],
Guo, Y.[Yi],
Wang, W.[Wei],
Hu, B.[Bin],
Postgraduate Student Depression Assessment by Multimedia Gait
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MultMedMag(29), No. 2, April 2022, pp. 56-65.
IEEE DOI
2208
Depression, Feature extraction, Mental health, Legged locomotion,
Data mining, Depression, Electroencephalography, Kinetic energy,
Fast Fourier transforms
BibRef
de Melo, W.C.[Wheidima Carneiro],
Granger, E.[Eric],
Hadid, A.[Abdenour],
A Deep Multiscale Spatiotemporal Network for Assessing Depression
From Facial Dynamics,
AffCom(13), No. 3, July 2022, pp. 1581-1592.
IEEE DOI
2209
Depression, Feature extraction, Deep learning, Face recognition,
Visualization, Convolution, Affective computing, multiscale processing
BibRef
Zhou, X.Z.[Xiu-Zhuang],
Wei, Z.Q.[Ze-Qiang],
Xu, M.[Min],
Qu, S.[Shan],
Guo, G.D.[Guo-Dong],
Facial Depression Recognition by Deep Joint Label Distribution and
Metric Learning,
AffCom(13), No. 3, July 2022, pp. 1605-1618.
IEEE DOI
2209
Feature extraction, Face recognition, Measurement,
Predictive models, Histograms, Spatiotemporal phenomena, Faces,
spatiotemporal feature
BibRef
Woodward, K.[Kieran],
Kanjo, E.[Eiman],
Brown, D.J.[David J.],
McGinnity, T.M.,
Inkster, B.[Becky],
Macintyre, D.J.[Donald J.],
Tsanas, A.[Athanasios],
Beyond Mobile Apps: A Survey of Technologies for Mental Well-Being,
AffCom(13), No. 3, July 2022, pp. 1216-1235.
IEEE DOI
2209
Stress, Monitoring, Tools, Sensors, Biomedical monitoring,
Stress measurement, Mood, Pervasive computing, mental well-being, health care
BibRef
Niu, M.Y.[Ming-Yue],
Zhao, Z.P.[Zi-Ping],
Tao, J.H.[Jian-Hua],
Li, Y.[Ya],
Schuller, B.W.[Björn W.],
Selective Element and Two Orders Vectorization Networks for Automatic
Depression Severity Diagnosis via Facial Changes,
CirSysVideo(32), No. 11, November 2022, pp. 8065-8077.
IEEE DOI
2211
Depression, Tensors, Feature extraction, Residual neural networks,
Spatiotemporal phenomena, Convolutional neural networks,
two orders vectorization block
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Chen, T.[Tao],
Guo, Y.R.[Yan-Rong],
Hao, S.J.[Shi-Jie],
Hong, R.C.[Ri-Chang],
Exploring Self-Attention Graph Pooling With EEG-Based Topological
Structure and Soft Label for Depression Detection,
AffCom(13), No. 4, October 2022, pp. 2106-2118.
IEEE DOI
2212
Brain modeling, Electroencephalography, Task analysis,
Feature extraction, Electrodes, Depression, Convolution,
soft label
BibRef
Kong, Y.Y.[You-Yong],
Niu, S.Y.[Shu-Yi],
Gao, H.[Heren],
Yue, Y.Y.[Ying-Ying],
Shu, H.Z.[Hua-Zhong],
Xie, C.M.[Chun-Ming],
Zhang, Z.J.[Zhi-Jun],
Yuan, Y.G.[Yong-Gui],
Multi-Stage Graph Fusion Networks for Major Depressive Disorder
Diagnosis,
AffCom(13), No. 4, October 2022, pp. 1917-1928.
IEEE DOI
2212
Convolution, Depression, Support vector machines, Grey matter,
Correlation, White matter, Brain modeling, Disease diagnosis,
white matter connectivity
BibRef
Kong, Y.Y.[You-Yong],
Wang, W.H.[Wen-Han],
Liu, X.Y.[Xiao-Yun],
Gao, S.W.[Shu-Wen],
Hou, Z.H.[Zheng-Hua],
Xie, C.M.[Chun-Ming],
Zhang, Z.J.[Zhi-Jun],
Yuan, Y.G.[Yong-Gui],
Multi-Connectivity Representation Learning Network for Major
Depressive Disorder Diagnosis,
MedImg(42), No. 10, October 2023, pp. 3012-3024.
IEEE DOI
2310
BibRef
Yadav, U.[Uma],
Sharma, A.K.[Ashish K.],
A novel automated depression detection technique using text
transcript,
IJIST(33), No. 1, 2023, pp. 108-122.
DOI Link
2301
BGRU, deep learning, depression detection, depression levels,
PHQ score, text embeddings, text transcripts
BibRef
Greco, C.M.[Candida M.],
Simeri, A.[Andrea],
Tagarelli, A.[Andrea],
Zumpano, E.[Ester],
Transformer-based language models for mental health issues: A survey,
PRL(167), 2023, pp. 204-211.
Elsevier DOI
2303
Transformers, Language models, Mental health, NLP, Deep learning, Benchmarks
BibRef
Li, J.X.[Jian-Xiu],
Hao, Y.R.[Yan-Rong],
Zhang, W.[Wei],
Li, X.W.[Xiao-Wei],
Hu, B.[Bin],
Effective Connectivity Based EEG Revealing the Inhibitory Deficits
for Distracting Stimuli in Major Depression Disorders,
AffCom(14), No. 1, January 2023, pp. 694-705.
IEEE DOI
2303
Faces, Electroencephalography, Monitoring, Task analysis, Electrodes,
Depression, Brain modeling, Dynamic causal modeling, EEG,
major depression disorders
BibRef
de Melo, W.C.[Wheidima Carneiro],
Granger, E.[Eric],
López, M.B.[Miguel Bordallo],
MDN: A Deep Maximization-Differentiation Network for Spatio-Temporal
Depression Detection,
AffCom(14), No. 1, January 2023, pp. 578-590.
IEEE DOI
2303
Depression, Solid modeling, Computational modeling,
Feature extraction, Face recognition, Affective computing,
depression detection
BibRef
Jayawardena, S.[Sadari],
Epps, J.[Julien],
Ambikairajah, E.[Eliathamby],
Ordinal Logistic Regression With Partial Proportional Odds for
Depression Prediction,
AffCom(14), No. 1, January 2023, pp. 563-577.
IEEE DOI
2303
Depression, Logistics, Predictive models, Computational modeling,
Reliability, Mathematical model, Machine learning,
model selection
BibRef
Niu, M.Y.[Ming-Yue],
Tao, J.H.[Jian-Hua],
Liu, B.[Bin],
Huang, J.[Jian],
Lian, Z.[Zheng],
Multimodal Spatiotemporal Representation for Automatic Depression
Level Detection,
AffCom(14), No. 1, January 2023, pp. 294-307.
IEEE DOI
2303
Feature extraction, Depression, Spatiotemporal phenomena,
Databases, Image segmentation, Multimodal depression detection,
multimodal attention feature fusion
BibRef
Aragón, M.E.[Mario Ezra],
López-Monroy, A.P.[Adrian Pastor],
González-Gurrola, L.C.[Luis Carlos],
Montes-y-Gómez, M.[Manuel],
Detecting Mental Disorders in Social Media Through Emotional Patterns:
The Case of Anorexia and Depression,
AffCom(14), No. 1, January 2023, pp. 211-222.
IEEE DOI
2303
Depression, Social networking (online), Mental disorders,
Mental health, Task analysis, Linguistics, Blogs, Mental disorders,
machine learning
BibRef
Schoene, A.M.[Annika Marie],
Turner, A.P.[Alexander P.],
de Mel, G.[Geeth],
Dethlefs, N.[Nina],
Hierarchical Multiscale Recurrent Neural Networks for Detecting
Suicide Notes,
AffCom(14), No. 1, January 2023, pp. 153-164.
IEEE DOI
2303
Linguistics, Social networking (online), Blogs, Task analysis,
Depression, Recurrent neural networks, Medical services,
text classification
BibRef
Alghowinem, S.[Sharifa],
Gedeon, T.[Tom],
Goecke, R.[Roland],
Cohn, J.F.[Jeffrey F.],
Parker, G.[Gordon],
Interpretation of Depression Detection Models via Feature Selection
Methods,
AffCom(14), No. 1, January 2023, pp. 133-152.
IEEE DOI
2303
Feature extraction, Depression, Biological system modeling,
Deep learning, Analytical models, Australia, Stability analysis,
datasets generalisation
BibRef
Pérez-Toro, P.A.[Paula Andrea],
Vásquez-Correa, J.C.[Juan Camilo],
Bocklet, T.[Tobias],
Nöth, E.[Elmar],
Orozco-Arroyave, J.R.[Juan Rafael],
User State Modeling Based on the Arousal-Valence Plane:
Applications in Customer Satisfaction and Health-Care,
AffCom(14), No. 2, April 2023, pp. 1533-1546.
IEEE DOI
2306
Acoustics, Diseases, Customer satisfaction, Linguistics, Depression,
Mood, Feature extraction, Arousal-valence plane, acoustic,
depression
BibRef
Allen, K.C.[Kristen C.],
Davis, A.[Alex],
Krishnamurti, T.[Tamar],
Indirect Identification of Perinatal Psychosocial Risks From Natural
Language,
AffCom(14), No. 2, April 2023, pp. 1506-1519.
IEEE DOI
2306
Depression, Feature extraction, Pregnancy, Mood, Predictive models,
Pediatrics, Social networking (online), Sentiment analysis,
methods for emotion elicitation
BibRef
Cao, L.[Lei],
Zhang, H.J.[Hui-Jun],
Wang, X.[Xin],
Feng, L.[Ling],
Learning Users Inner Thoughts and Emotion Changes for Social Media
Based Suicide Risk Detection,
AffCom(14), No. 2, April 2023, pp. 1280-1296.
IEEE DOI
2306
Blogs, Social networking (online), Correlation, Task analysis,
Feature extraction, Tools, Mirrors, Suicide risk detection, emotion change
BibRef
Dehshibi, M.M.[Mohammad Mahdi],
Baiani, B.[Bita],
Pons, G.[Gerard],
Masip, D.[David],
A Deep Multimodal Learning Approach to Perceive Basic Needs of Humans
From Instagram Profile,
AffCom(14), No. 2, April 2023, pp. 944-956.
IEEE DOI
2306
Multimedia Web sites, Visualization, Social networking (online),
Depression, Mental health, Feature extraction, Feeds, Social media,
bag of content
BibRef
Dhelim, S.[Sahraoui],
Chen, L.M.[Li-Ming],
Das, S.K.[Sajal K.],
Ning, H.S.[Huan-Sheng],
Nugent, C.[Chris],
Leavey, G.[Gerard],
Pesch, D.[Dirk],
Bantry-White, E.[Eleanor],
Burns, D.[Devin],
Detecting Mental Distresses Using Social Behavior Analysis in the
Context of COVID-19: A Survey,
Surveys(55), No. 14s, July 2023, pp. xx-yy.
DOI Link
2309
Survey, Mental Health. mental disorder detection, COVID-19, mental health, Social media analysis
BibRef
Zuo, L.[Lishi],
Mak, M.W.[Man-Wai],
Avoiding dominance of speaker features in speech-based depression
detection,
PRL(173), 2023, pp. 50-56.
Elsevier DOI
2310
Depression detection, Speaker invariance,
Feature disentanglement, Speaker embedding
BibRef
Mao, K.[Kaining],
Zhang, W.[Wei],
Wang, D.B.F.[Deborah Bao-Feng],
Li, A.[Ang],
Jiao, R.Q.[Rong-Qi],
Zhu, Y.H.[Yan-Hui],
Wu, B.[Bin],
Zheng, T.S.[Tian-Sheng],
Qian, L.[Lei],
Lyu, W.[Wei],
Ye, M.J.[Min-Jie],
Chen, J.[Jie],
Prediction of Depression Severity Based on the Prosodic and Semantic
Features With Bidirectional LSTM and Time Distributed CNN,
AffCom(14), No. 3, July 2023, pp. 2251-2265.
IEEE DOI
2310
BibRef
Uddin, M.A.[Md Azher],
Joolee, J.B.[Joolekha Bibi],
Sohn, K.A.[Kyung-Ah],
Deep Multi-Modal Network Based Automated Depression Severity
Estimation,
AffCom(14), No. 3, July 2023, pp. 2153-2167.
IEEE DOI
2310
BibRef
Li, J.X.[Jian-Xiu],
Li, N.[Nan],
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Chen, J.[Junhao],
Hao, Y.R.[Yan-Rong],
Li, X.W.[Xiao-Wei],
Hu, B.[Bin],
Altered Brain Dynamics and Their Ability for Major Depression
Detection Using EEG Microstates Analysis,
AffCom(14), No. 3, July 2023, pp. 2116-2126.
IEEE DOI
2310
BibRef
Zhu, J.[Jing],
Yang, C.[Changlin],
Xie, X.[Xiannian],
Wei, S.Q.[Shi-Qing],
Li, Y.Z.[Yi-Zhou],
Li, X.W.[Xiao-Wei],
Hu, B.[Bin],
Mutual Information Based Fusion Model (MIBFM): Mild Depression
Recognition Using EEG and Pupil Area Signals,
AffCom(14), No. 3, July 2023, pp. 2102-2115.
IEEE DOI
2310
BibRef
Niu, M.Y.[Ming-Yue],
Zhao, Z.[Ziping],
Tao, J.H.[Jian-Hua],
Li, Y.[Ya],
Schuller, B.W.[Björn W.],
Dual Attention and Element Recalibration Networks for Automatic
Depression Level Prediction,
AffCom(14), No. 3, July 2023, pp. 1954-1965.
IEEE DOI
2310
BibRef
Shang, Y.Y.[Yuan-Yuan],
Pan, Y.C.[Yu-Chen],
Jiang, X.[Xiao],
Shao, Z.H.[Zhu-Hong],
Guo, G.D.[Guo-Dong],
Liu, T.[Tie],
Ding, H.[Hui],
LQGDNet: A Local Quaternion and Global Deep Network for Facial
Depression Recognition,
AffCom(14), No. 3, July 2023, pp. 2557-2563.
IEEE DOI
2310
BibRef
Watanabe, Y.[Yoshio],
Taniguchi, A.[Akira],
Tomimoto, H.[Hidekazu],
Maximum isotope accumulation in the retrosplenial cortex during
amnesia attack and its temporal change suggest cortical spreading
depression as a pathophysiology of patients with transient global
amnesia,
IJIST(33), No. 6, 2023, pp. 1902-1913.
DOI Link
2311
Brodmann area 7, cortical spreading depolarization,
posterior cingulate cortex, retrosplenial cortex, transient global amnesia
BibRef
Casado, C.Á.[Constantino Álvarez],
Cańellas, M.L.[Manuel Lage],
López, M.B.[Miguel Bordallo],
Depression Recognition Using Remote Photoplethysmography From Facial
Videos,
AffCom(14), No. 4, October 2023, pp. 3305-3316.
IEEE DOI
2312
BibRef
Zou, B.[Bochao],
Han, J.L.[Jia-Li],
Wang, Y.X.[Ying-Xue],
Liu, R.[Rui],
Zhao, S.[Shenghui],
Feng, L.[Lei],
Lyu, X.[Xiangwen],
Ma, H.M.[Hui-Min],
Semi-Structural Interview-Based Chinese Multimodal Depression Corpus
Towards Automatic Preliminary Screening of Depressive Disorders,
AffCom(14), No. 4, October 2023, pp. 2823-2838.
IEEE DOI
2312
BibRef
Sun, H.[Hao],
Chen, Y.W.[Yen-Wei],
Lin, L.[Lanfen],
TensorFormer: A Tensor-Based Multimodal Transformer for Multimodal
Sentiment Analysis and Depression Detection,
AffCom(14), No. 4, October 2023, pp. 2776-2786.
IEEE DOI
2312
BibRef
Zheng, W.B.[Wen-Bo],
Yan, L.[Lan],
Wang, F.Y.[Fei-Yue],
Two Birds With One Stone: Knowledge-Embedded Temporal Convolutional
Transformer for Depression Detection and Emotion Recognition,
AffCom(14), No. 4, October 2023, pp. 2595-2613.
IEEE DOI
2312
BibRef
Chen, T.[Tao],
Hong, R.C.[Ri-Chang],
Guo, Y.R.[Yan-Rong],
Hao, S.J.[Shi-Jie],
Hu, B.[Bin],
MS˛-GNN: Exploring GNN-Based Multimodal Fusion Network for Depression
Detection,
Cyber(53), No. 12, December 2023, pp. 7749-7759.
IEEE DOI
2312
BibRef
Mao, Y.[Yu],
Wei, D.[Dongtao],
Yang, W.J.[Wen-Jing],
Chen, Q.[Qunlin],
Sun, J.Z.[Jiang-Zhou],
Yu, Y.[Yaxu],
Li, Y.[Yu],
Zhuang, K.X.[Kai-Xiang],
Wang, X.Q.[Xiao-Qin],
He, L.[Li],
Feng, T.Y.[Ting-Yong],
Lei, X.[Xu],
He, Q.H.[Qing-Hua],
Chen, H.[Hong],
Qin, S.Z.[Shao-Zheng],
Liu, Y.Z.[Yun-Zhe],
Qiu, J.[Jiang],
A Neural Predictive Model of Negative Emotions for COVID-19,
AffCom(14), No. 4, October 2023, pp. 2646-2656.
IEEE DOI
2312
BibRef
Chen, T.[Tao],
Guo, Y.R.[Yan-Rong],
Hao, S.J.[Shi-Jie],
Hong, R.C.[Ri-Chang],
Semi-Supervised Domain Adaptation for Major Depressive Disorder
Detection,
MultMed(26), 2024, pp. 3567-3579.
IEEE DOI
2402
Optimization, Feature extraction, Uncertainty, Task analysis,
Noise measurement, Adaptation models, Semantics, Class imbalance,
uncertainty estimation
BibRef
Zhang, J.[Jun],
Guo, Y.R.[Yan-Rong],
Multilevel depression status detection based on fine-grained prompt
learning,
PRL(178), 2024, pp. 167-173.
Elsevier DOI
2402
Fine-grained depression detection,
Question-response-level modeling, Prompt learning
BibRef
Niu, M.Y.[Ming-Yue],
Tao, J.H.[Jian-Hua],
Li, Y.W.[Yong-Wei],
Qin, Y.[Yong],
Li, Y.[Ya],
WavDepressionNet: Automatic Depression Level Prediction via Raw
Speech Signals,
AffCom(15), No. 1, January 2024, pp. 285-296.
IEEE DOI
2403
Depression, Spectrogram, Feature extraction, Speech processing,
Predictive models, Databases, Convolution, Assessment block,
WavDepressionNet
BibRef
Teng, S.Y.[Shi-Yu],
Liu, J.Q.[Jia-Qing],
Huang, Y.[Yue],
Chai, S.R.[Shu-Rong],
Tateyama, T.[Tomoko],
Huang, X.Y.[Xin-Yin],
Lin, L.F.[Lan-Fen],
Chen, Y.W.[Yen-Wei],
An Intra- and Inter-Emotion Transformer-Based Fusion Model with
Homogeneous and Diverse Constraints Using Multi-Emotional Audiovisual
Features for Depression Detection,
IEICE(E108-D), No. 3, March 2024, pp. 342-353.
WWW Link.
2403
BibRef
Liu, L.L.[Liang-Liang],
Liu, Z.H.[Zhi-Hong],
Chang, J.[Jing],
Xu, X.[Xue],
A multi-modal extraction integrated model for neuropsychiatric
disorders classification,
PR(155), 2024, pp. 110646.
Elsevier DOI
2408
Integrated model, Neuropsychiatric disorder, Multi-scale,
Interpretability, Classification
BibRef
Liu, M.J.[Meng-Jun],
Zhang, H.F.[Hui-Feng],
Liu, M.X.[Mian-Xin],
Chen, D.D.[Dong-Dong],
Zhou, R.[Rubai],
Lu, W.X.[Wen-Xian],
Zhang, L.[Lichi],
Shen, D.G.[Ding-Gang],
Wang, Q.[Qian],
Peng, D.[Daihui],
Hierarchical Encoding and Fusion of Brain Functions for Depression
Subtype Classification,
AffCom(15), No. 3, July 2024, pp. 1826-1837.
IEEE DOI
2409
Depression, Feature extraction, Encoding, Correlation,
Brain modeling, Graph neural networks, Diseases,
augmentation regularization
BibRef
Ye, J.[Jiayu],
Yu, Y.H.[Yan-Hong],
Zheng, Y.[Yunshao],
Liu, Y.[Yang],
Wang, Q.X.[Qing-Xiang],
Dep-FER: Facial Expression Recognition in Depressed Patients Based on
Voluntary Facial Expression Mimicry,
AffCom(15), No. 3, July 2024, pp. 1725-1738.
IEEE DOI
2409
Depression, Face recognition, Task analysis, Faces,
Feature extraction, Affective computing, Uncertainty, FER
BibRef
Jung, M.[Myeongul],
Cho, Y.[Youngwug],
Kim, J.[Jejoong],
Kim, H.[Hyungsook],
Kim, K.[Kwanguk],
Bodily Sensation Map vs. Bodily Motion Map: Visualizing and Analyzing
Emotional Body Motions,
AffCom(15), No. 3, July 2024, pp. 1649-1658.
IEEE DOI
2409
Emotion recognition, Motion capture, Depression,
Affective computing, Particle measurements, Mental disorders, visualization
BibRef
Li, F.[Fenghua],
Liu, G.X.[Guo-Xiong],
Zou, Z.L.[Zhi-Ling],
Yan, Y.[Yang],
Huang, X.[Xin],
Liu, X.[Xuanang],
Liu, Z.K.[Zheng-Kui],
A Classification Framework for Depressive Episode Using R-R Intervals
From Smartwatch,
AffCom(15), No. 3, July 2024, pp. 1387-1399.
IEEE DOI
2409
Mood, Predictive models, Data models,
Wearable Health Monitoring Systems, Monitoring, digital mental health
BibRef
Zhang, S.Q.[Shi-Qing],
Zhang, X.[Xingnan],
Zhao, X.M.[Xiao-Ming],
Fang, J.X.[Jiang-Xiong],
Niu, M.Y.[Ming-Yue],
Zhao, Z.[Ziping],
Yu, J.[Jun],
Tian, Q.[Qi],
MTDAN: A Lightweight Multi-Scale Temporal Difference Attention
Networks for Automated Video Depression Detection,
AffCom(15), No. 3, July 2024, pp. 1078-1089.
IEEE DOI
2409
Depression, Behavioral sciences, Feature extraction, Deep learning,
Computational modeling, Task analysis, Computational complexity,
computational complexity
BibRef
Hsu, J.H.[Jia-Hao],
Wu, C.H.[Chung-Hsien],
Wang, W.K.[Wei-Kai],
Su, H.Y.[Hung-Yi],
Lin, E.C.L.[Esther Ching-Lan],
Chen, P.S.[Po See],
Digital Phenotyping-Based Bipolar Disorder Assessment Using Multiple
Correlation Data Imputation and Lasso-MLP,
AffCom(15), No. 3, July 2024, pp. 885-897.
IEEE DOI
2409
Depression, Predictive models, Mood, Data models, Medical services,
Correlation, Smart phones, Bipolar disorder (BD), data imputation,
lasso-MLP
BibRef
Pan, Y.C.[Yu-Chen],
Shang, Y.Y.[Yuan-Yuan],
Shao, Z.[Zhuhong],
Liu, T.[Tie],
Guo, G.D.[Guo-Dong],
Ding, H.[Hui],
Integrating Deep Facial Priors Into Landmarks for Privacy Preserving
Multimodal Depression Recognition,
AffCom(15), No. 3, July 2024, pp. 828-836.
IEEE DOI
2409
Depression, Feature extraction, Face recognition, Visualization,
Training, Image recognition, Deep learning, Depression recognition,
video recognition
BibRef
Shen, H.T.[Hao-Tian],
Song, S.Y.[Si-Yang],
Gunes, H.[Hatice],
Multi-modal Human Behaviour Graph Representation Learning for
Automatic Depression Assessment,
FG24(1-10)
IEEE DOI
2408
Representation learning, Codes, Face recognition,
Gesture recognition, Depression, Feature extraction, Behavioral sciences
BibRef
Mesquita, F.[Francisco],
Maurício, J.[José],
Marques, G.[Gonçalo],
Depression Detection Using Deep Learning and Natural Language
Processing Techniques: A Comparative Study,
CIARP23(I:327-342).
Springer DOI
2312
BibRef
Wei, P.C.[Ping-Cheng],
Peng, K.Y.[Kun-Yu],
Roitberg, A.[Alina],
Yang, K.L.[Kai-Lun],
Zhang, J.M.[Jia-Ming],
Stiefelhagen, R.[Rainer],
Multi-modal Depression Estimation Based on Sub-attentional Fusion,
ACVR22(623-639).
Springer DOI
2304
BibRef
Loecher, N.[Nele],
King, S.[Sayde],
Cabo, J.[Joseph],
Neal, T.[Tempestt],
Kosyluk, K.[Kristin],
Assessing the Efficacy of a Self-Stigma Reduction Mental Health
Program with Mobile Biometrics: Work-in-Progress,
FG23(1-6)
IEEE DOI
2303
Costs, Face recognition, Computational modeling, Education, Merging,
Mental health, Gesture recognition
BibRef
Bilalpur, M.[Maneesh],
Hinduja, S.[Saurabh],
Cariola, L.A.[Laura A.],
Sheeber, L.B.[Lisa B.],
Alien, N.[Nick],
Jeni, L.A.[László A.],
Morency, L.P.[Louis-Philippe],
Cohn, J.F.[Jeffrey F.],
Multimodal Feature Selection for Detecting Mothers' Depression in
Dyadic Interactions with their Adolescent Offspring,
FG23(1-8)
IEEE DOI
2303
Psychology, Production, Gesture recognition, Depression,
Feature extraction, Problem-solving, History
BibRef
Tran, M.[Minh],
Bradley, E.[Ellen],
Matvey, M.[Michelle],
Woolley, J.[Joshua],
Soleymani, M.[Mohammad],
Modeling Dynamics of Facial Behavior for Mental Health Assessment,
FG21(1-5)
IEEE DOI
2303
Heuristic algorithms, Face recognition, Clustering algorithms,
Estimation, Mental health, Gesture recognition, Depression
BibRef
Darzi, A.[Ali],
Provenza, N.R.[Nicole R.],
Jeni, L.A.[László A.],
Borton, D.A.[David A.],
Sheth, S.A.[Sameer A.],
Goodman, W.K.[Wayne K.],
Cohn, J.F.[Jeffrey F.],
Facial Action Units and Head Dynamics in Longitudinal Interviews
Reveal OCD and Depression severity and DBS Energy,
FG21(1-6)
IEEE DOI
2303
Face recognition, Satellite broadcasting, Biomarkers,
Predictive models, Depression, Magnetic heads, Physiology
BibRef
Chen, M.Z.[Ming-Zhe],
Xiao, X.[Xi],
Zhang, B.[Bin],
Liu, X.Y.[Xin-Yu],
Lu, R.[Runiu],
Neural Architecture Searching for Facial Attributes-based Depression
Recognition,
ICPR22(877-884)
IEEE DOI
2212
Face recognition, Redundancy, Mental health, Feature extraction,
Depression, Solids, Data structures
BibRef
Fu, X.Y.[Xiao-Yan],
Li, J.M.[Jin-Ming],
Liu, H.[Honghong],
Zhang, M.M.[Miao-Miao],
Xin, G.[Ge],
Audio Signal-based Depression Level Prediction Combining Temporal and
Spectral Features,
ICPR22(359-365)
IEEE DOI
2212
Heuristic algorithms, Mental disorders,
Frequency-domain analysis, Predictive models, Depression, Deep Networks
BibRef
Zubiaga, I.[Irune],
Justo, R.[Raquel],
Multimodal Feature Evaluation and Fusion for Emotional Well-Being
Monitorization,
IbPRIA22(242-254).
Springer DOI
2205
BibRef
Lin, Y.X.[Yu-Xin],
Ma, H.M.[Hui-Min],
Pan, Z.[Zeyu],
Wang, R.Q.[Rong-Quan],
Depression Detection by Combining Eye Movement with Image Semantics,
ICIP21(269-273)
IEEE DOI
2201
Support vector machines, Deep learning, Image segmentation,
Mental disorders, Design methodology, Semantics, Psychology,
semantic segmentation
BibRef
Wei, C.[Chao],
Lu, K.[Ke],
Gan, W.[Wei],
Xue, J.[Jian],
Spatiotemporal Features and Local Relationship Learning for Facial
Action Unit Intensity Regression,
ICIP21(1109-1113)
IEEE DOI
2201
Gold, Emotion recognition, Convolution, Feature extraction,
Depression, Encoding, Facial expression, action unit intensity,
multi-label regression
BibRef
Othmani, A.[Alice],
Kadoch, D.[Daoud],
Bentounes, K.[Kamil],
Rejaibi, E.[Emna],
Alfred, R.[Romain],
Hadid, A.[Abdenour],
Towards Robust Deep Neural Networks for Affect and Depression
Recognition from Speech,
CAIHA20(5-19).
Springer DOI
2103
BibRef
Ahmad, D.[Dua'a],
Goecke, R.[Roland],
Ireland, J.[James],
Cnn Depression Severity Level Estimation from Upper Body vs. Face-only
Images,
MPRSS20(744-758).
Springer DOI
2103
BibRef
Neal, T.,
Canavan, S.,
Mood Versus Identity:
Studying the Influence of Affective States on Mobile Biometrics,
FG20(562-566)
IEEE DOI
2102
Mood, Biometrics (access control), Task analysis,
Emotion recognition, Support vector machines, Face recognition,
smartphones
BibRef
Bera, A.,
Randhavane, T.,
Prinja, R.,
Kapsaskis, K.,
Wang, A.,
Gray, K.,
Manocha, D.,
How are you feeling? Multimodal Emotion Learning for
Socially-Assistive Robot Navigation,
FG20(644-651)
IEEE DOI
2102
Robots, Navigation, Trajectory, Collision avoidance,
Prediction algorithms, Computational modeling, Legged locomotion,
humanoid
BibRef
Zhang, Z.,
Lin, W.,
Liu, M.,
Mahmoud, M.,
Multimodal Deep Learning Framework for Mental Disorder Recognition,
FG20(344-350)
IEEE DOI
2102
Mental disorders, Feature extraction, Interviews, Visualization,
Noise reduction, Encoding, Acoustics, mental disorder, deep learning,
multimodal
BibRef
Jin, J.,
Huang, L.,
A Region-Based Feature Extraction Method for Rs-fMRI of Depressive
Disorder Classification,
CVIDL20(707-710)
IEEE DOI
2102
biomedical MRI, brain, diseases, feature extraction,
image classification, learning (artificial intelligence),
computer aided diagnosis
BibRef
Lin, W.,
Orton, I.,
Liu, M.,
Mahmoud, M.,
Automatic Detection of Self-Adaptors for Psychological Distress,
FG20(371-378)
IEEE DOI
2102
Feature extraction, Depression, Psychology, Detectors, Encoding,
Videos, Task analysis, psychological distress, fidgeting,
machine learning
BibRef
Pan, Z.,
Ma, H.,
Zhang, L.,
Wang, Y.,
Depression Detection Based on Reaction Time and Eye Movement,
ICIP19(2184-2188)
IEEE DOI
1910
Depression detection, reaction time, eye movement, attention bias
BibRef
de Melo, W.C.,
Granger, E.,
Hadid, A.,
Depression Detection Based on Deep Distribution Learning,
ICIP19(4544-4548)
IEEE DOI
1910
Affective Computing, Depression Detection,
Deep Distribution Learning, Convolutional Neural Nets.
BibRef
Hussain, S.A.[Syed Ali],
Park, T.[Taiwoo],
Yildirim, I.[Irem],
Xiang, Z.H.[Zi-Han],
Abbasi, F.[Farha],
Virtual-Reality Videos to Relieve Depression,
VAMR18(II: 77-85).
Springer DOI
1807
BibRef
Liao, D.[Dan],
Shu, L.[Lin],
Huang, Y.P.[Yan-Ping],
Yang, J.[Jiong],
Xu, X.M.[Xiang-Min],
Scenes Design in Virtual Reality for Depression Assessment,
VAMR18(II: 116-125).
Springer DOI
1807
BibRef
Song, S.,
Shen, L.,
Valstar, M.,
Human Behaviour-Based Automatic Depression Analysis Using
Hand-Crafted Statistics and Deep Learned Spectral Features,
FG18(158-165)
IEEE DOI
1806
Encoding, Estimation, Face, Feature extraction, Gold, Standards,
Time series analysis, Automatic depression analysis,
Statistic
BibRef
Eigbe, N.,
Baltrusaitis, T.,
Morency, L.P.,
Pestian, J.,
Toward Visual Behavior Markers of Suicidal Ideation,
FG18(530-534)
IEEE DOI
1806
Hospitals, Interviews, Mental disorders, Protocols, Task analysis,
Tools, Visualization, Depression, Diagnosis, Facial behaviors, Gaze,
Visual behaviors
BibRef
Anis, K.,
Zakia, H.,
Mohamed, D.,
Jeffrey, C.,
Detecting Depression Severity by Interpretable Representations of
Motion Dynamics,
FG18(739-745)
IEEE DOI
1806
Dynamics, Encoding, Face, Kinematics, Machine learning, Magnetic heads,
Shape, Barycentric coordinates, Depression Severity Assessment,
face and head dynamics
BibRef
Ma, X.C.[Xing-Chen],
Huang, D.[Di],
Wang, Y.H.[Yun-Hong],
Wang, Y.D.[Yi-Ding],
Cost-Sensitive Two-Stage Depression Prediction Using Dynamic Visual
Clues,
ACCV16(II: 338-351).
Springer DOI
1704
BibRef
Alghowinem, S.[Sharifa],
Goecke, R.[Roland],
Cohn, J.F.,
Wagner, M.[Michael],
Parker, G.[Gordon],
Breakspear, M.[Michael],
Cross-cultural detection of depression from nonverbal behaviour,
FG15(1-8)
IEEE DOI
1508
BibRef
Earlier: A1, A2, A4, A5, A6, Only:
Eye movement analysis for depression detection,
ICIP13(4220-4224)
IEEE DOI
1402
cultural aspects.
Eye movement; active appearance model; affective sensing; shape analysis
BibRef
Joshi, J.[Jyoti],
Goecke, R.[Roland],
Parker, G.[Gordon],
Breakspear, M.[Michael],
Can body expressions contribute to automatic depression analysis?,
FG13(1-7)
IEEE DOI
1309
face recognition
BibRef
Joshi, J.[Jyoti],
Dhall, A.[Abhinav],
Goecke, R.[Roland],
Breakspear, M.[Michael],
Parker, G.[Gordon],
Neural-net classification for spatio-temporal descriptor based
depression analysis,
ICPR12(2634-2638).
WWW Link.
1302
BibRef
Chapter on Face Recognition, Detection, Tracking, Gesture Recognition, Fingerprints, Biometrics continues in
Personality, Traits, Mood, Deception, Diagnosis Analysis .