22.3.6.2.4 Depression Analysis, PTSD, Mental Health

Chapter Contents (Back)
Application, Faces. Mood. depression.
See also Emotion Recognition from Face Images.
See also Emotions in Face Animation, Video Face Synthesis.
See also Multi-Modal Emotion, Multimodal Emotion Recognition.

Yang, Y.[Ying], Fairbairn, C.[Catherine], Cohn, J.F.[Jeffrey F.],
Detecting Depression Severity from Vocal Prosody,
AffCom(4), No. 2, 2013, pp. 142-150.
IEEE DOI 1307
Atmospheric measurements BibRef

Nguyen, T.[Thin], Phung, D.Q.[Dinh Q.], Dao, B.[Bo], Venkatesh, S., Berk, M.,
Affective and Content Analysis of Online Depression Communities,
AffCom(5), No. 3, July 2014, pp. 217-226.
IEEE DOI 1412
feature extraction BibRef

Girard, J.M.[Jeffrey M.], Cohn, J.F.[Jeffrey F.], Mahoor, M.H.[Mohammad H.], Mavadati, S.M.[S. Mohammad], Hammal, Z.[Zakia], Rosenwald, D.P.[Dean P.],
Nonverbal social withdrawal in depression: Evidence from manual and automatic analyses,
IVC(32), No. 10, 2014, pp. 641-647.
Elsevier DOI 1410
BibRef
Earlier: A1, A2, A3, A4, A6, Only:
Social risk and depression: Evidence from manual and automatic facial expression analysis,
FG13(1-8)
IEEE DOI 1309
Depression. behavioural sciences computing BibRef

Tittmann, M.[Mandy], Günther, T.[Thomas], Sacher, J.[Julia], Himmerich, H.[Hubertus], Villringer, A.[Arno], Hegerl, U.[Ulrich], Schönknecht, P.[Peter],
Structural brain changes in early-onset and late-onset depression: An update of volumetric MRI findings,
IJIST(24), No. 2, 2014, pp. 149-160.
DOI Link 1405
MRI BibRef

Lopez-Otero, P.[Paula], Docio-Fernandez, L.[Laura], Garcia-Mateo, C.[Carmen],
Assessing speaker independence on a speech-based depression level estimation system,
PRL(68, Part 2), No. 1, 2015, pp. 343-350.
Elsevier DOI 1512
Soft biometrics BibRef

Scherer, S., Lucas, G.M., Gratch, J., Rizzo, A.S.[A. Skip], Morency, L.P.,
Self-Reported Symptoms of Depression and PTSD Are Associated with Reduced Vowel Space in Screening Interviews,
AffCom(7), No. 1, January 2016, pp. 59-73.
IEEE DOI 1603
Acoustics BibRef

Stratou, G.[Giota], Morency, L.P.[Louis-Philippe],
MultiSense: Context-Aware Nonverbal Behavior Analysis Framework: A Psychological Distress Use Case,
AffCom(8), No. 2, April 2017, pp. 190-203.
IEEE DOI 1706
Affective computing, Computer architecture, Context, Pipelines, Psychology, Real-time systems, Synchronization, MultiSense, automatic distress assessment, behavior quantification, framework for multimodal behavioral understanding, system, for, affective, computing BibRef

Venek, V.[Verena], Scherer, S.[Stefan], Morency, L.P.[Louis-Philippe], Rizzo, A.S.[Albert Skip], Pestian, J.[John],
Adolescent Suicidal Risk Assessment in Clinician-Patient Interaction,
AffCom(8), No. 2, April 2017, pp. 204-215.
IEEE DOI 1706
Feature extraction, Interviews, Pediatrics, Repeaters, Risk management, Speech, Support vector machines, Behavior analytics, clinician-patient interaction, hierarchical classifiers, ubiquitous questions, youth, suicide BibRef

Lin, W., Wu, H., Liu, Y., Lv, D., Yang, L.,
A CCA and ICA-Based Mixture Model for Identifying Major Depression Disorder,
MedImg(36), No. 3, March 2017, pp. 745-756.
IEEE DOI 1703
Analytical models BibRef

Walczak, N.[Nicholas], Fasching, J.[Joshua], Cullen, K.[Kathryn], Morellas, V.[Vassilios], Papanikolopoulos, N.[Nikolaos],
Toward identifying behavioral risk markers for mental health disorders: an assistive system for monitoring children's movements in a preschool classroom,
MVA(29), No. 4, May 2018, pp. 703-717.
Springer DOI 1805
BibRef

Zhu, Y., Shang, Y., Shao, Z., Guo, G.,
Automated Depression Diagnosis Based on Deep Networks to Encode Facial Appearance and Dynamics,
AffCom(9), No. 4, October 2018, pp. 578-584.
IEEE DOI 1812
Face recognition, Feature extraction, Histograms, Optical imaging, Databases, Optical computing, Automated depression diagnosis, flow dynamics BibRef

Alghowinem, S., Goecke, R., Wagner, M., Epps, J., Hyett, M., Parker, G., Breakspear, M.,
Multimodal Depression Detection: Fusion Analysis of Paralinguistic, Head Pose and Eye Gaze Behaviors,
AffCom(9), No. 4, October 2018, pp. 478-490.
IEEE DOI 1812
Feature extraction, Speech, Australia, Sensors, Magnetic heads, Mood, Depression detection, multimodal fusion, speaking behaviour, head pose BibRef

Wang, Q.X.[Qing-Xiang], Yang, H.X.[Huan-Xin], Yu, Y.H.[Yan-Hong],
Facial expression video analysis for depression detection in Chinese patients,
JVCIR(57), 2018, pp. 228-233.
Elsevier DOI 1812
Depression detection, Facial expression, Video processing, Eye movement, Feature extraction BibRef

Huang, K.Y.[Kun-Yi], Wu, C.H.[Chung-Hsien], Su, M.H.[Ming-Hsiang],
Attention-based convolutional neural network and long short-term memory for short-term detection of mood disorders based on elicited speech responses,
PR(88), 2019, pp. 668-678.
Elsevier DOI 1901
Mood disorder detection, Convolutional neural network, Long short-term memory, Attention model BibRef

He, L., Jiang, D., Sahli, H.,
Automatic Depression Analysis Using Dynamic Facial Appearance Descriptor and Dirichlet Process Fisher Encoding,
MultMed(21), No. 6, June 2019, pp. 1476-1486.
IEEE DOI 1906
Feature extraction, Databases, Histograms, Visualization, Encoding, Face, Face recognition, Depression, nonverbal behaviors, Dirichlet process Fisher vector (DPFV) BibRef

Pampouchidou, A., Simos, P.G., Marias, K., Meriaudeau, F., Yang, F., Pediaditis, M., Tsiknakis, M.,
Automatic Assessment of Depression Based on Visual Cues: A Systematic Review,
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 BibRef

Qureshi, S.A., Saha, S., Hasanuzzaman, M., Dias, G.,
Multitask Representation Learning for Multimodal Estimation of Depression Level,
IEEE_Int_Sys(34), No. 5, September 2019, pp. 45-52.
IEEE DOI 1912
Learning systems, Affective computing, Estimation, Depression, Medical conditions BibRef

Wongaptikaseree, K.[Konlakorn], Yomaboot, P.[Panida], Katchapakirin, K.[Kantinee], Kaewpitakkun, Y.[Yongyos],
Social Behavior Analysis and Thai Mental Health Questionnaire (TMHQ) Optimization for Depression Detection System,
IEICE(E103-D), No. 4, April 2020, pp. 771-778.
WWW Link. 2004
BibRef

Pampouchidou, A., Pediaditis, M., Kazantzaki, E., Sfakianakis, S., Apostolaki, I.A., Argyraki, K., Manousos, D., Meriaudeau, F., Marias, K., Yang, F., Tsiknakis, M., Basta, M., Vgontzas, A.N., Simos, P.,
Automated facial video-based recognition of depression and anxiety symptom severity: cross-corpus validation,
MVA(31), No. 4, April 2020, pp. Article30.
Springer DOI 2005
BibRef

Acharya, D.[Divya], Goel, S.[Shivani], Asthana, R.[Rishi], Bhardwaj, A.[Arpit],
A novel fitness function in genetic programming to handle unbalanced emotion recognition data,
PRL(133), 2020, pp. 272-279.
Elsevier DOI 2005
Emotion recognition, Fitness function, Genetic programming, EEG, Fast Fourier transformation BibRef

Cummins, N., Sethu, V., Epps, J., Williamson, J.R., Quatieri, T.F., Krajewski, J.,
Generalized Two-Stage Rank Regression Framework for Depression Score Prediction from Speech,
AffCom(11), No. 2, April 2020, pp. 272-283.
IEEE DOI 2006
Speech, Feature extraction, Acoustics, Indexes, Tools, Predictive models, Signal processing, Bayesian, two-stage regression BibRef

Alsagri, H.S.[Hatoon S.], Ykhlef, M.[Mourad],
Machine Learning-Based Approach for Depression Detection in Twitter Using Content and Activity Features,
IEICE(E103-D), No. 8, August 2020, pp. 1825-1832.
WWW Link. 2008
BibRef

Cai, H., Zhang, X., Zhang, Y., Wang, Z., Hu, B.,
A Case-Based Reasoning Model for Depression Based on Three-Electrode EEG Data,
AffCom(11), No. 3, July 2020, pp. 383-392.
IEEE DOI 2008
Electroencephalography, Brain modeling, Feature extraction, Cognition, Medical services, Physiology, Electrodes, health implications BibRef

Huang, K., Wu, C., Su, M., Kuo, Y.,
Detecting Unipolar and Bipolar Depressive Disorders from Elicited Speech Responses Using Latent Affective Structure Model,
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 BibRef

Zhou, X., Jin, K., Shang, Y., Guo, G.,
Visually Interpretable Representation Learning for Depression Recognition from Facial Images,
AffCom(11), No. 3, July 2020, pp. 542-552.
IEEE DOI 2008
Visualization, Feature extraction, Videos, Face recognition, Face, Computer architecture, Image recognition, Depression recognition, depression activation map BibRef

Yang, L., Jiang, D., Sahli, H.,
Integrating Deep and Shallow Models for Multi-Modal Depression Analysis: Hybrid Architectures,
AffCom(12), No. 1, January 2021, pp. 239-253.
IEEE DOI 2103
Visualization, Estimation, Support vector machines, Feature extraction, Histograms, Analytical models, Neural networks, histogram of displacement range (HDR) BibRef

Al Jazaery, M., Guo, G.,
Video-Based Depression Level Analysis by Encoding Deep Spatiotemporal Features,
AffCom(12), No. 1, January 2021, pp. 262-268.
IEEE DOI 2103
Face, Visualization, Spatiotemporal phenomena, Recurrent neural networks, Feature extraction, recurrent neural network (RNN) BibRef

Chen, Q.[Qian], Chaturvedi, I.[Iti], Ji, S.X.[Shao-Xiong], Cambria, E.[Erik],
Sequential fusion of facial appearance and dynamics for depression recognition,
PRL(150), 2021, pp. 115-121.
Elsevier DOI 2109
Depression recognition, Facial representation, Convolutional neural network, Multimodal learning, Sequential fusion BibRef

Hong, Q.B.[Qian-Bei], Wu, C.H.[Chung-Hsien], Su, M.H.[Ming-Hsiang], Chang, C.C.[Chia-Cheng],
Exploring Macroscopic and Microscopic Fluctuations of Elicited Facial Expressions for Mood Disorder Classification,
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 BibRef

Han, J.[Jing], Zhang, Z.X.[Zi-Xing], Mascolo, C.[Cecilia], André, E.[Elisabeth], Tao, J.H.[Jian-Hua], Zhao, Z.[Ziping], 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 BibRef

Chiong, R.[Raymond], Budhi, G.S.[Gregorious Satia], Dhakal, S.[Sandeep],
Combining Sentiment Lexicons and Content-Based Features for Depression Detection,
IEEE_Int_Sys(36), No. 6, November 2021, pp. 99-105.
IEEE DOI 2112
Sentiment analysis, Social networking (online), Depression, Feature extraction, Boosting, Intelligent systems, Standards BibRef

Shen, J.[Jian], Zhang, X.W.[Xiao-Wei], Wang, G.[Gang], Ding, Z.J.[Zhi-Jie], Hu, B.[Bin],
An Improved Empirical Mode Decomposition of Electroencephalogram Signals for Depression Detection,
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 BibRef

Liu, J.[Jie], Dey, N.[Nilanjan], Crespo, R.G.[Ruben González], Shi, F.Q.[Fu-Qian], Liu, C.[Chanjuan],
Inadequate dataset learning for major depressive disorder MRI semantic classification,
IET-IPR(16), No. 6, 2022, pp. 1648-1656.
DOI Link 2204
BibRef

Huang, Z.C.[Zhao-Cheng], Epps, J.[Julien], Joachim, D.[Dale],
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 BibRef

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

Song, S.Y.[Si-Yang], Jaiswal, S.[Shashank], Shen, L.L.[Lin-Lin], Valstar, M.[Michel],
Spectral Representation of Behaviour Primitives for Depression Analysis,
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], ud Din, Z.[Zia], 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 Analysis,
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 BibRef

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.[Shuwen], 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], Shao, X.X.[Xue-Xiao], 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


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.[Zihan], 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 .


Last update:Mar 16, 2024 at 20:36:19