{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,3]],"date-time":"2026-06-03T14:07:37Z","timestamp":1780495657143,"version":"3.54.1"},"publisher-location":"Cham","reference-count":40,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031376597","type":"print"},{"value":"9783031376603","type":"electronic"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023]]},"DOI":"10.1007\/978-3-031-37660-3_49","type":"book-chapter","created":{"date-parts":[[2023,7,29]],"date-time":"2023-07-29T06:02:20Z","timestamp":1690610540000},"page":"691-702","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["MMDA: A Multimodal Dataset for\u00a0Depression and\u00a0Anxiety Detection"],"prefix":"10.1007","author":[{"given":"Yueqi","family":"Jiang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ziyang","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiao","family":"Sun","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,7,30]]},"reference":[{"key":"49_CR1","unstructured":"Organization, W.H.: Depression key facts [EB\/OL]. https:\/\/www.who.int\/news-room\/fact-sheets\/detail\/depression\/. Accessed 13 Sept 2021"},{"issue":"3","key":"49_CR2","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1016\/S2215-0366(18)30511-X","volume":"6","author":"Y Huang","year":"2019","unstructured":"Huang, Y., et al.: Prevalence of mental disorders in China: a cross-sectional epidemiological study. Lancet Psychiatry 6(3), 211\u2013224 (2019)","journal-title":"Lancet Psychiatry"},{"key":"49_CR3","doi-asserted-by":"crossref","unstructured":"Shen, G., et al.: Depression detection via harvesting social media: a multimodal dictionary learning solution. In: IJCAI, pp. 3838\u20133844 (2017)","DOI":"10.24963\/ijcai.2017\/536"},{"key":"49_CR4","doi-asserted-by":"crossref","unstructured":"Xezonaki, D., Paraskevopoulos, G., Potamianos, A., Narayanan, S.: Affective conditioning on hierarchical attention networks applied to depression detection from transcribed clinical interviews. In: INTERSPEECH, pp. 4556\u20134560 (2020)","DOI":"10.21437\/Interspeech.2020-2819"},{"key":"49_CR5","doi-asserted-by":"publisher","first-page":"904","DOI":"10.1016\/j.jad.2021.08.090","volume":"295","author":"J Ye","year":"2021","unstructured":"Ye, J., et al.: Multi-modal depression detection based on emotional audio and evaluation text. J. Affect. Disord. 295, 904\u2013913 (2021)","journal-title":"J. Affect. Disord."},{"key":"49_CR6","doi-asserted-by":"crossref","unstructured":"Guo, W., Yang, H., Liu, Z.: Deep neural networks for depression recognition based on facial expressions caused by stimulus tasks. In: 2019 8th International Conference on Affective Computing and Intelligent Interaction Workshops and Demos (ACIIW), pp. 133\u2013139. IEEE (2019)","DOI":"10.1109\/ACIIW.2019.8925293"},{"key":"49_CR7","unstructured":"Haque, A., Guo, M., Miner, A.S., Fei-Fei, L.: Measuring depression symptom severity from spoken language and 3D facial expressions. arXiv preprint arXiv:1811.08592 (2018)"},{"issue":"4","key":"49_CR8","doi-asserted-by":"publisher","first-page":"478","DOI":"10.1109\/TAFFC.2016.2634527","volume":"9","author":"S Alghowinem","year":"2016","unstructured":"Alghowinem, S., et al.: Multimodal depression detection: fusion analysis of paralinguistic, head pose and eye gaze behaviors. IEEE Trans. Affect. Comput. 9(4), 478\u2013490 (2016)","journal-title":"IEEE Trans. Affect. Comput."},{"issue":"1","key":"49_CR9","doi-asserted-by":"publisher","first-page":"56","DOI":"10.1136\/jnnp.23.1.56","volume":"23","author":"M Hamilton","year":"1960","unstructured":"Hamilton, M.: A rating scale for depression. J. Neurol. Neurosurg. Psychiatry 23(1), 56 (1960)","journal-title":"J. Neurol. Neurosurg. Psychiatry"},{"issue":"1","key":"49_CR10","doi-asserted-by":"publisher","first-page":"50","DOI":"10.1111\/j.2044-8341.1959.tb00467.x","volume":"32","author":"M Hamilton","year":"1959","unstructured":"Hamilton, M.: The assessment of anxiety states by rating. Br. J. Med. Psychol. 32(1), 50\u201355 (1959)","journal-title":"Br. J. Med. Psychol."},{"key":"49_CR11","series-title":"Learning and Analytics in Intelligent Systems","doi-asserted-by":"publisher","first-page":"25","DOI":"10.1007\/978-3-030-80571-5_3","volume-title":"Advances in Artificial Intelligence-based Technologies","author":"G Gaudi","year":"2022","unstructured":"Gaudi, G., Kapralos, B., Collins, K.C., Quevedo, A.: Affective computing: an introduction to the detection, measurement, and current applications. In: Virvou, M., Tsihrintzis, G.A., Tsoukalas, L.H., Jain, L.C. (eds.) Advances in Artificial Intelligence-based Technologies. LAIS, vol. 22, pp. 25\u201343. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-030-80571-5_3"},{"key":"49_CR12","unstructured":"Maas, A.L., Daly, R.E., Pham, P.T., Huang, D., Ng, A.Y., Potts, C.: Learning word vectors for sentiment analysis. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pp. 142\u2013150. Association for Computational Linguistics, Portland (2011), http:\/\/www.aclweb.org\/anthology\/P11-1015"},{"key":"49_CR13","doi-asserted-by":"crossref","unstructured":"Zhang, Z., Luo, P., Loy, C.C., Tang, X.: Learning social relation traits from face images. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3631\u20133639 (2015)","DOI":"10.1109\/ICCV.2015.414"},{"issue":"2","key":"49_CR14","doi-asserted-by":"publisher","first-page":"479","DOI":"10.1109\/TAFFC.2018.2884461","volume":"12","author":"JA Miranda-Correa","year":"2018","unstructured":"Miranda-Correa, J.A., Abadi, M.K., Sebe, N., Patras, I.: Amigos: A dataset for affect, personality and mood research on individuals and groups. IEEE Trans. Affect. Comput. 12(2), 479\u2013493 (2018)","journal-title":"IEEE Trans. Affect. Comput."},{"issue":"1","key":"49_CR15","doi-asserted-by":"publisher","first-page":"42","DOI":"10.1109\/T-AFFC.2011.25","volume":"3","author":"M Soleymani","year":"2011","unstructured":"Soleymani, M., Lichtenauer, J., Pun, T., Pantic, M.: A multimodal database for affect recognition and implicit tagging. IEEE Trans. Affect. Comput. 3(1), 42\u201355 (2011)","journal-title":"IEEE Trans. Affect. Comput."},{"issue":"1","key":"49_CR16","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1109\/T-AFFC.2011.20","volume":"3","author":"G Mckeown","year":"2013","unstructured":"Mckeown, G.: The semaine database: annotated multimodal records of emotionally colored conversations between a person and a limited agent. IEEE Trans. Affect. Comput. 3(1), 5\u201317 (2013)","journal-title":"IEEE Trans. Affect. Comput."},{"key":"49_CR17","doi-asserted-by":"crossref","unstructured":"Gong, Y., Poellabauer, C.: Topic modeling based multi-modal depression detection. In: Proceedings of the 7th Annual Workshop on Audio\/Visual Emotion Challenge, pp. 69\u201376 (2017)","DOI":"10.1145\/3133944.3133945"},{"key":"49_CR18","doi-asserted-by":"crossref","unstructured":"Zhang, Z., Lin, W., Liu, M., Mahmoud, M.: Multimodal deep learning framework for mental disorder recognition. In: 2020 15th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2020), pp. 344\u2013350. IEEE (2020)","DOI":"10.1109\/FG47880.2020.00033"},{"key":"49_CR19","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.pmcj.2018.09.003","volume":"51","author":"E Garcia-Ceja","year":"2018","unstructured":"Garcia-Ceja, E., Riegler, M., Nordgreen, T., Jakobsen, P., Oedegaard, K.J., T\u00f8rresen, J.: Mental health monitoring with multimodal sensing and machine learning: a survey. Pervasive Mob. Comput. 51, 1\u201326 (2018)","journal-title":"Pervasive Mob. Comput."},{"key":"49_CR20","doi-asserted-by":"crossref","unstructured":"\u00c7ift\u00e7i, E., Kaya, H., G\u00fcle\u00e7, H., Salah, A.A.: The turkish audio-visual bipolar disorder corpus. In: 2018 First Asian Conference on Affective Computing and Intelligent Interaction (ACII Asia), pp. 1\u20136. IEEE (2018)","DOI":"10.1109\/ACIIAsia.2018.8470362"},{"key":"49_CR21","unstructured":"Gratch, J., et al.: The distress analysis interview corpus of human and computer interviews. In: Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC\u201914), pp. 3123\u20133128 (2014)"},{"key":"49_CR22","unstructured":"DeVault, D., et al.: Simsensei kiosk: A virtual human interviewer for healthcare decision support. In: Proceedings of the 2014 International Conference on Autonomous Agents and Multi-Agent Systems, pp. 1061\u20131068 (2014)"},{"issue":"2","key":"49_CR23","doi-asserted-by":"publisher","first-page":"525","DOI":"10.1109\/JBHI.2017.2676878","volume":"22","author":"H Dibeklio\u011flu","year":"2017","unstructured":"Dibeklio\u011flu, H., Hammal, Z., Cohn, J.F.: Dynamic multimodal measurement of depression severity using deep autoencoding. IEEE J. Biomed. Health Inform. 22(2), 525\u2013536 (2017)","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"49_CR24","unstructured":"Cai, H., et al.: Modma dataset: a multi-modal open dataset for mental-disorder analysis. arXiv preprint arXiv:2002.09283 (2020)"},{"key":"49_CR25","doi-asserted-by":"crossref","unstructured":"Spitzer, R.L., Kroenke, K., Williams, J.B., Group, P.H.Q.P.C.S., Group, P.H.Q.P.C.S., et al.: Validation and utility of a self-report version of PRIME-MD: the PHQ primary care study. JAMA 282(18), 1737\u20131744 (1999)","DOI":"10.1001\/jama.282.18.1737"},{"key":"49_CR26","doi-asserted-by":"publisher","first-page":"105701","DOI":"10.1109\/ACCESS.2019.2932393","volume":"7","author":"Y Xing","year":"2019","unstructured":"Xing, Y., et al.: Task-state heart rate variability parameter-based depression detection model and effect of therapy on the parameters. IEEE Access 7, 105701\u2013105709 (2019)","journal-title":"IEEE Access"},{"key":"49_CR27","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2019.103381","volume":"112","author":"S Byun","year":"2019","unstructured":"Byun, S., et al.: Detection of major depressive disorder from linear and nonlinear heart rate variability features during mental task protocol. Comput. Biol. Med. 112, 103381 (2019)","journal-title":"Comput. Biol. Med."},{"key":"49_CR28","first-page":"1","volume":"2018","author":"H Cai","year":"2018","unstructured":"Cai, H., et al.: A pervasive approach to EEG-based depression detection. Complexity 2018, 1\u201313 (2018)","journal-title":"Complexity"},{"issue":"3","key":"49_CR29","doi-asserted-by":"publisher","first-page":"429","DOI":"10.1109\/TNSRE.2019.2894423","volume":"27","author":"S Sun","year":"2019","unstructured":"Sun, S., et al.: Graph theory analysis of functional connectivity in major depression disorder with high-density resting state EEG data. IEEE Trans. Neural Syst. Rehabil. Eng. 27(3), 429\u2013439 (2019)","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"49_CR30","doi-asserted-by":"publisher","first-page":"636","DOI":"10.1016\/j.jad.2018.05.071","volume":"238","author":"JT Fiquer","year":"2018","unstructured":"Fiquer, J.T., Moreno, R.A., Brunoni, A.R., Barros, V.B., Fernandes, F., Gorenstein, C.: What is the nonverbal communication of depression? assessing expressive differences between depressive patients and healthy volunteers during clinical interviews. J. Affect. Disord. 238, 636\u2013644 (2018)","journal-title":"J. Affect. Disord."},{"key":"49_CR31","doi-asserted-by":"crossref","unstructured":"Baltrusaitis, T., Zadeh, A., Lim, Y.C., Morency, L.P.: Openface 2.0: facial behavior analysis toolkit. In: 2018 13th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2018), pp. 59\u201366. IEEE (2018)","DOI":"10.1109\/FG.2018.00019"},{"issue":"2","key":"49_CR32","doi-asserted-by":"publisher","first-page":"142","DOI":"10.1109\/T-AFFC.2012.38","volume":"4","author":"Y Yang","year":"2012","unstructured":"Yang, Y., Fairbairn, C., Cohn, J.F.: Detecting depression severity from vocal prosody. IEEE Trans. Affect. Comput. 4(2), 142\u2013150 (2012)","journal-title":"IEEE Trans. Affect. Comput."},{"key":"49_CR33","doi-asserted-by":"publisher","first-page":"214","DOI":"10.1016\/j.jad.2017.08.038","volume":"225","author":"T Taguchi","year":"2018","unstructured":"Taguchi, T., et al.: Major depressive disorder discrimination using vocal acoustic features. J. Affect. Disord. 225, 214\u2013220 (2018)","journal-title":"J. Affect. Disord."},{"key":"49_CR34","doi-asserted-by":"crossref","unstructured":"Low, L.S.A., Maddage, N.C., Lech, M., Allen, N.: Mel frequency cepstral feature and gaussian mixtures for modeling clinical depression in adolescents. In: 2009 8th IEEE International Conference on Cognitive Informatics, pp. 346\u2013350. IEEE (2009)","DOI":"10.1109\/COGINF.2009.5250714"},{"issue":"3","key":"49_CR35","doi-asserted-by":"publisher","first-page":"574","DOI":"10.1109\/TBME.2010.2091640","volume":"58","author":"LSA Low","year":"2010","unstructured":"Low, L.S.A., Maddage, N.C., Lech, M., Sheeber, L.B., Allen, N.B.: Detection of clinical depression in adolescents\u2019 speech during family interactions. IEEE Trans. Biomed. Eng. 58(3), 574\u2013586 (2010)","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"49_CR36","doi-asserted-by":"crossref","unstructured":"Eyben, F., W\u00f6llmer, M., Schuller, B.: Opensmile: the munich versatile and fast open-source audio feature extractor. In: Proceedings of the 18th ACM International Conference on Multimedia, pp. 1459\u20131462 (2010)","DOI":"10.1145\/1873951.1874246"},{"key":"49_CR37","doi-asserted-by":"crossref","unstructured":"Ive, J., Gkotsis, G., Dutta, R., Stewart, R., Velupillai, S.: Hierarchical neural model with attention mechanisms for the classification of social media text related to mental health. In: Proceedings of the Fifth Workshop on Computational Linguistics and Clinical Psychology: From Keyboard to Clinic, pp. 69\u201377 (2018)","DOI":"10.18653\/v1\/W18-0607"},{"key":"49_CR38","doi-asserted-by":"crossref","unstructured":"Sekuli\u0107, I., Strube, M.: Adapting deep learning methods for mental health prediction on social media. arXiv preprint arXiv:2003.07634 (2020)","DOI":"10.18653\/v1\/D19-5542"},{"issue":"4","key":"49_CR39","doi-asserted-by":"publisher","first-page":"152","DOI":"10.2478\/popets-2019-0063","volume":"2019","author":"J Weerasinghe","year":"2019","unstructured":"Weerasinghe, J., Morales, K., Greenstadt, R.: \u201cBecause... I was told... so much\u2019\u2019: linguistic indicators of mental health status on twitter. Proc. Priv. Enhancing Technol. 2019(4), 152\u2013171 (2019)","journal-title":"Proc. Priv. Enhancing Technol."},{"issue":"13","key":"49_CR40","first-page":"1","volume":"34","author":"S Ji","year":"2021","unstructured":"Ji, S., Li, X., Huang, Z., Cambria, E.: Suicidal ideation and mental disorder detection with attentive relation networks. Neural Comput. Appl. 34(13), 1\u201311 (2021)","journal-title":"Neural Comput. Appl."}],"container-title":["Lecture Notes in Computer Science","Pattern Recognition, Computer Vision, and Image Processing. ICPR 2022 International Workshops and Challenges"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-37660-3_49","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,7,29]],"date-time":"2023-07-29T06:10:34Z","timestamp":1690611034000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-37660-3_49"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031376597","9783031376603"],"references-count":40,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-37660-3_49","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"30 July 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICPR","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Pattern Recognition","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Montr\u00e9al, QC","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Canada","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"21 August 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"25 August 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icpr2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/iapr.org\/icpr2022","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}