{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,4]],"date-time":"2026-01-04T11:04:30Z","timestamp":1767524670217,"version":"3.48.0"},"reference-count":230,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,1,4]],"date-time":"2026-01-04T00:00:00Z","timestamp":1767484800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,1,4]],"date-time":"2026-01-04T00:00:00Z","timestamp":1767484800000},"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":["Iran J Comput Sci"],"published-print":{"date-parts":[[2026,12]]},"DOI":"10.1007\/s42044-025-00359-0","type":"journal-article","created":{"date-parts":[[2026,1,4]],"date-time":"2026-01-04T09:42:38Z","timestamp":1767519758000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A review of multimodal depression detection methods using smartphone usage and audio-visual clues"],"prefix":"10.1007","volume":"9","author":[{"given":"Thati Ravi","family":"Prasad","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Praveen","family":"Kumar","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,1,4]]},"reference":[{"key":"359_CR1","unstructured":"Organization, W., et al.: The impact of COVID-19 on mental, neurological and substance use services: results of a rapid assessment. World health organization (2020)"},{"key":"359_CR2","doi-asserted-by":"publisher","first-page":"129","DOI":"10.1016\/j.jpsychires.2021.09.054","volume":"144","author":"O Renaud-Charest","year":"2021","unstructured":"Renaud-Charest, O., Lui, L., Eskander, S., Ceban, F., Ho, R., Di Vincenzo, J., Rosenblat, J., Lee, Y., Subramaniapillai, M., McIntyre, R.: Onset and frequency of depression in post-COVID-19 syndrome: a systematic review. J. Psychiatr. Res. 144, 129\u2013137 (2021). (https:\/\/www.sciencedirect.com\/science\/article\/pii\/S002239562100594X)","journal-title":"J. Psychiatr. Res."},{"key":"359_CR3","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijchp.2020.07.007","volume":"21","author":"J Bueno-Notivol","year":"2021","unstructured":"Bueno-Notivol, J., Gracia-Garc, P., Olaya, B., Lasheras, I., Lopez-Anton, R., Santabarbara, J.: Prevalence of depression during the COVID-19 outbreak: a meta-analysis of community-based studies. Int. J. Clin. Health Psychol. 21, 100196 (2021)","journal-title":"Int. J. Clin. Health Psychol."},{"key":"359_CR4","doi-asserted-by":"publisher","first-page":"55","DOI":"10.1016\/j.jad.2020.08.001","volume":"277","author":"J Xiong","year":"2020","unstructured":"Xiong, J., Lipsitz, O., Nasri, F., Lui, L., Gill, H., Phan, L., Chen-Li, D., Iacobucci, M., Ho, R., Majeed, A., McIntyre, R.: Impact of COVID-19 pandemic on mental health in the general population: a systematic review. J. Affect. Disord. 277, 55\u201364 (2020). (https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0165032720325891)","journal-title":"J. Affect. Disord."},{"key":"359_CR5","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s40359-023-01130-5","volume":"11","author":"I Kupcova","year":"2023","unstructured":"Kupcova, I., Danisovic, L., Klein, M., Harsanyi, S.: Effects of the COVID-19 pandemic on mental health, anxiety, and depression. BMC Psychol. 11, 1\u20137 (2023)","journal-title":"BMC Psychol."},{"key":"359_CR6","doi-asserted-by":"publisher","first-page":"S439","DOI":"10.1016\/S0924-977X(00)00111-5","volume":"10","author":"Y Lecrubier","year":"2000","unstructured":"Lecrubier, Y.: Depressive illness and disability. Eur. Neuropsychopharmacol. 10, S439\u2013S443 (2000)","journal-title":"Eur. Neuropsychopharmacol."},{"key":"359_CR7","doi-asserted-by":"publisher","first-page":"437","DOI":"10.1192\/bjp.117.539.437","volume":"117","author":"S Guze","year":"1970","unstructured":"Guze, S., Robins, E.: Suicide and primary affective disorders. Br. J. Psychiatry 117, 437\u2013438 (1970)","journal-title":"Br. J. Psychiatry"},{"key":"359_CR8","unstructured":"WHO Mental health preparedness and response for the COVID-19 pandemic Report by the Director-General. https:\/\/unsdg.un.org\/resources\/policy-brief-covid-19-and-need-action-mental-health (2021)"},{"key":"359_CR9","doi-asserted-by":"publisher","first-page":"1569","DOI":"10.1111\/j.1742-1241.2007.01423.x","volume":"61","author":"M Falagas","year":"2007","unstructured":"Falagas, M., Vardakas, K., Vergidis, P.: Under-diagnosis of common chronic diseases: prevalence and impact on human health. Int. J. Clin. Pract. 61, 1569\u20131579 (2007)","journal-title":"Int. J. Clin. Pract."},{"key":"359_CR10","doi-asserted-by":"publisher","first-page":"5","DOI":"10.4103\/IJAM.IJAM_49_17","volume":"3","author":"T Butryn","year":"2017","unstructured":"Butryn, T., Bryant, L., Marchionni, C., Sholevar, F.: The shortage of psychiatrists and other mental health providers: causes, current state, and potential solutions. Int. J. Acad. Med. 3, 5 (2017)","journal-title":"Int. J. Acad. Med."},{"key":"359_CR11","doi-asserted-by":"publisher","first-page":"120","DOI":"10.1017\/S1092852900017430","volume":"7","author":"S Arbabzadeh-Bouchez","year":"2002","unstructured":"Arbabzadeh-Bouchez, S., Tylee, A., L\u00e9pine, J.: A European perspective on depression in the community: the DEPRES study. CNS Spectr. 7, 120\u2013126 (2002)","journal-title":"CNS Spectr."},{"key":"359_CR12","doi-asserted-by":"publisher","first-page":"253","DOI":"10.1016\/j.cbpra.2015.04.002","volume":"22","author":"J Comer","year":"2015","unstructured":"Comer, J.: Introduction to the special series: applying new technologies to extend the scope and accessibility of mental health care. Cognitive Behav. Pract. 22, 253\u2013257 (2015). (https:\/\/www.sciencedirect.com\/science\/article\/pii\/S1077722915000267)","journal-title":"Cognitive Behav. Pract."},{"key":"359_CR13","doi-asserted-by":"publisher","first-page":"492","DOI":"10.1016\/j.beth.2020.07.002","volume":"52","author":"E Thompson","year":"2021","unstructured":"Thompson, E., Destree, L., Albertella, L., Fontenelle, L.: Internet-based acceptance and commitment therapy: a transdiagnostic systematic review and meta-analysis for mental health outcomes. Behav. Ther. 52, 492\u2013507 (2021). https:\/\/doi.org\/10.1016\/j.beth.2020.07.002","journal-title":"Behav. Ther."},{"key":"359_CR14","doi-asserted-by":"crossref","unstructured":"Sundaravadivel, P., Salvatore, P., Indic, P.: M-SID: An IoT-based edge-intelligent framework for suicidal ideation detection. 2020 IEEE 6th World Forum On Internet Of Things (WF-IoT). pp. 1\u20136 (2020)","DOI":"10.1109\/WF-IoT48130.2020.9221279"},{"key":"359_CR15","doi-asserted-by":"publisher","first-page":"612","DOI":"10.1111\/jnu.12423","volume":"50","author":"S Chen","year":"2018","unstructured":"Chen, S., Jones, C., Moyle, W.: Social robots for depression in older adults: a systematic review. J. Nurs. Scholarsh. 50, 612\u2013622 (2018)","journal-title":"J. Nurs. Scholarsh."},{"key":"359_CR16","doi-asserted-by":"crossref","unstructured":"Goyal, S., Bedi, P., Garg, N.: AR and VR and AI Allied Technologies and Depression Detection and Control Mechanism. Computational Intelligence Techniques For Combating COVID-19. pp. 203\u2013229 (2021)","DOI":"10.1007\/978-3-030-68936-0_11"},{"key":"359_CR17","doi-asserted-by":"publisher","first-page":"478","DOI":"10.1109\/TAFFC.2016.2634527","volume":"9","author":"S Alghowinem","year":"2016","unstructured":"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. IEEE Trans. Affect. Comput. 9, 478\u2013490 (2016)","journal-title":"IEEE Trans. Affect. Comput."},{"key":"359_CR18","doi-asserted-by":"publisher","first-page":"838","DOI":"10.1177\/1745691616650285","volume":"11","author":"S Gosling","year":"2016","unstructured":"Gosling, S., Harari, G., Crosier, B., Lane, N., Wang, R., Campbell, A.: Using smartphones to collect behavioral data in psychological science. Perspect. Psychol. Sci. 11, 838\u2013854 (2016)","journal-title":"Perspect. Psychol. Sci."},{"key":"359_CR19","first-page":"16","volume":"28","author":"Y Fukazawa","year":"2020","unstructured":"Fukazawa, Y., Yamamoto, N., Hamatani, T., Ochiai, K., Uchiyama, A., Ohta, K.: Smartphone-based mental state estimation: A survey from a machine learning perspective. J. Inf. Process. 28, 16\u201330 (2020)","journal-title":"J. Inf. Process."},{"key":"359_CR20","doi-asserted-by":"publisher","DOI":"10.1016\/j.jbi.2019.103139","volume":"92","author":"Y Can","year":"2019","unstructured":"Can, Y., Arnrich, B., Ersoy, C.: Stress detection in daily life scenarios using smart phones and wearable sensors: a survey. J. Biomed. Inform. 92, 103139 (2019). https:\/\/doi.org\/10.1016\/j.jbi.2019.103139","journal-title":"J. Biomed. Inform."},{"key":"359_CR21","doi-asserted-by":"crossref","unstructured":"Kelly, D., Caulfield, B.: An investigation into non-invasive physical activity recognition using smartphones. Proceedings Of The Annual International Conference Of The IEEE Engineering in Medicine and Biology Society, EMBS. pp. 3340\u20133343 (2012)","DOI":"10.1109\/EMBC.2012.6346680"},{"key":"359_CR22","doi-asserted-by":"crossref","unstructured":"Morales, M., Scherer, S., Levitan, R.: A Cross-modal Review of Indicators for Depression Detection Systems. (2017)","DOI":"10.18653\/v1\/W17-3101"},{"key":"359_CR23","unstructured":"Khan, I., Gupta, R.: Early depression detection using ensemble machine learning framework. International Journal Of Information Technology. pp. 1\u20138 (2024)"},{"key":"359_CR24","doi-asserted-by":"publisher","first-page":"38819","DOI":"10.1007\/s11042-023-16221-z","volume":"83","author":"S Khan","year":"2024","unstructured":"Khan, S., Alqahtani, S.: Hybrid machine learning models to detect signs of depression. Multimed. Tools Appl. 83, 38819\u201338837 (2024)","journal-title":"Multimed. Tools Appl."},{"key":"359_CR25","doi-asserted-by":"publisher","DOI":"10.1111\/exsy.12409","volume":"36","author":"I Fatima","year":"2019","unstructured":"Fatima, I., Abbasi, B., Khan, S., Al-Saeed, M., Ahmad, H., Mumtaz, R.: Prediction of postpartum depression using machine learning techniques from social media text. Expert. Syst. 36, e12409 (2019)","journal-title":"Expert. Syst."},{"key":"359_CR26","doi-asserted-by":"crossref","unstructured":"Jiang, H., Hu, B., Liu, Z., Wang, G., Zhang, L., Li, X., Kang, H.: Detecting Depression Using an Ensemble Logistic Regression Model Based on Multiple Speech Features. Computational And Mathematical Methods In Medicine. 2018 (2018)","DOI":"10.1155\/2018\/6508319"},{"key":"359_CR27","doi-asserted-by":"publisher","first-page":"255","DOI":"10.1093\/fampra\/cmr092","volume":"29","author":"I Schumann","year":"2012","unstructured":"Schumann, I., Schneider, A., Kantert, C., L\u00f6we, B., Linde, K.: Physicians\u2019 attitudes, diagnostic process and barriers regarding depression diagnosis in primary care: a systematic review of qualitative studies. Fam. Pract. 29, 255\u2013263 (2012)","journal-title":"Fam. Pract."},{"key":"359_CR28","doi-asserted-by":"crossref","unstructured":"Ray, A., Kumar, S., Reddy, R., Mukherjee, P., Garg, R.: Multi-level attention network using text, audio and video for depression prediction. Proceedings Of The 9th International On Audio\/visual Emotion Challenge And Workshop. pp. 81\u201388 (2019)","DOI":"10.1145\/3347320.3357697"},{"key":"359_CR29","doi-asserted-by":"crossref","unstructured":"Nasir, M., Jati, A., Shivakumar, P., Nallan Chakravarthula, S., Georgiou, P.: Multimodal and multiresolution depression detection from speech and facial landmark features. Proceedings Of The 6th International Workshop On Audio\/visual Emotion Challenge. pp. 43\u201350 (2016)","DOI":"10.1145\/2988257.2988261"},{"key":"359_CR30","doi-asserted-by":"publisher","DOI":"10.1016\/j.jbi.2019.103151","volume":"93","author":"Y Fukazawa","year":"2019","unstructured":"Fukazawa, Y., Ito, T., Okimura, T., Yamashita, Y., Maeda, T., Ota, J.: Predicting anxiety state using smartphone-based passive sensing. J. Biomed. Inform. 93, 103151 (2019)","journal-title":"J. Biomed. Inform."},{"key":"359_CR31","doi-asserted-by":"publisher","DOI":"10.1016\/j.smhl.2019.100093","volume":"15","author":"S Ware","year":"2020","unstructured":"Ware, S., Yue, C., Morillo, R., Lu, J., Shang, C., Bi, J., Kamath, J., Russell, A., Bamis, A., Wang, B.: Predicting depressive symptoms using smartphone data. Smart Health. 15, 100093 (2020)","journal-title":"Smart Health."},{"key":"359_CR32","doi-asserted-by":"crossref","unstructured":"Chillarige, R., Distefano, S., Rawat, S.: Advances in Computational Intelligence and Informatics: Proceedings of ICACII 2023. (Springer Nature Singapore, 2024), https:\/\/books.google.co.in\/books?id=P8MbEQAAQBAJ","DOI":"10.1007\/978-981-97-4727-6"},{"key":"359_CR33","doi-asserted-by":"crossref","unstructured":"Shatte, A., Hutchinson, D., Teague, S.: Machine learning in mental health: a systematic scoping review of methods and applications. (2018)","DOI":"10.31219\/osf.io\/hjrw8"},{"key":"359_CR34","doi-asserted-by":"publisher","first-page":"606","DOI":"10.1046\/j.1525-1497.2001.016009606.x","volume":"16","author":"K Kroenke","year":"2001","unstructured":"Kroenke, K., Spitzer, R., Williams, J.: The PHQ-9: validity of a brief depression severity measure. J. Gen. Intern. Med. 16, 606\u2013613 (2001)","journal-title":"J. Gen. Intern. Med."},{"key":"359_CR35","doi-asserted-by":"publisher","first-page":"561","DOI":"10.1001\/archpsyc.1961.01710120031004","volume":"4","author":"A Beck","year":"1961","unstructured":"Beck, A., Ward, C., Mendelson, M., Mock, J., Erbaugh, J.: An inventory for measuring depression. Arch. Gen. Psychiatry 4, 561\u2013571 (1961)","journal-title":"Arch. Gen. Psychiatry"},{"key":"359_CR36","doi-asserted-by":"crossref","unstructured":"Hamilton, M.: The Hamilton rating scale for depression. Assessment Of Depression. pp. 143\u2013152 (1986)","DOI":"10.1007\/978-3-642-70486-4_14"},{"key":"359_CR37","doi-asserted-by":"publisher","first-page":"573","DOI":"10.1016\/S0006-3223(02)01866-8","volume":"54","author":"R AJ","year":"2003","unstructured":"AJ, R.: The 16-item quick inventory of depressive symptomatology (QIDS), clinician rating (QIDS-C), and self-report (QIDS-SR): a psychometric evaluation in patients with chronic major depression. Biol. Psychiatry 54, 573\u2013583 (2003)","journal-title":"Biol. Psychiatry"},{"key":"359_CR38","doi-asserted-by":"crossref","unstructured":"Blanken, G., Dittmann, J., Grimm, H., Marshall, J., Wallesch, C.: Linguistic disorders and pathologies: An international handbook. (Walter de Gruyter, 2008)","DOI":"10.1515\/9783110203370"},{"key":"359_CR39","doi-asserted-by":"publisher","first-page":"122","DOI":"10.1037\/0033-2909.102.1.122","volume":"102","author":"T Pyszczynski","year":"1987","unstructured":"Pyszczynski, T., Greenberg, J.: Self-regulatory perseveration and the depressive self-focusing style: a self-awareness theory of reactive depression. Psychol. Bull. 102, 122 (1987)","journal-title":"Psychol. Bull."},{"key":"359_CR40","unstructured":"Cummins, N.: Automatic assessment of depression from speech: paralinguistic analysis, modelling and machine learning. (2016)"},{"key":"359_CR41","doi-asserted-by":"publisher","first-page":"803","DOI":"10.3758\/s13428-016-0743-z","volume":"49","author":"S Crossley","year":"2017","unstructured":"Crossley, S., Kyle, K., McNamara, D.: Sentiment analysis and social cognition engine (SEANCE): an automatic tool for sentiment, social cognition, and social-order analysis. Behav. Res. Methods 49, 803\u2013821 (2017)","journal-title":"Behav. Res. Methods"},{"key":"359_CR42","doi-asserted-by":"crossref","unstructured":"Degottex, G., Kane, J., Drugman, T., Raitio, T., Scherer, S.: COVAREP\u2013a collaborative voice analysis repository for speech technologies. 2014 IEEE International Conference On Acoustics, Speech And Signal Processing (icassp). pp. 960\u2013964 (2014)","DOI":"10.1109\/ICASSP.2014.6853739"},{"key":"359_CR43","doi-asserted-by":"crossref","unstructured":"Eyben, F., W\u00f6llmer, M., Schuller, B.: Opensmile: the munich versatile and fast open-source audio feature extractor. Proceedings Of The 18th ACM International Conference On Multimedia. pp. 1459-1462 (2010)","DOI":"10.1145\/1873951.1874246"},{"key":"359_CR44","doi-asserted-by":"publisher","first-page":"10","DOI":"10.1016\/j.specom.2015.03.004","volume":"71","author":"N Cummins","year":"2015","unstructured":"Cummins, N., Scherer, S., Krajewski, J., Schnieder, S., Epps, J., Quatieri, T.: A review of depression and suicide risk assessment using speech analysis. Speech Commun. 71, 10\u201349 (2015)","journal-title":"Speech Commun."},{"key":"359_CR45","doi-asserted-by":"crossref","unstructured":"Yang, L., Jiang, D., He, L., Pei, E., Oveneke, M., Sahli, H.: Decision tree based depression classification from audio video and language information. Proceedings Of The 6th International Workshop On Audio\/visual Emotion Challenge. pp. 89\u201396 (2016)","DOI":"10.1145\/2988257.2988269"},{"key":"359_CR46","doi-asserted-by":"crossref","unstructured":"Al Hanai, T., Ghassemi, M., Glass, J.: Detecting depression with audio\/text sequence modeling of interviews. Interspeech. pp. 1716\u20131720 (2018)","DOI":"10.21437\/Interspeech.2018-2522"},{"key":"359_CR47","doi-asserted-by":"crossref","unstructured":"Zhang, L., Driscol, J., Chen, X., Hosseini Ghomi, R.: Evaluating acoustic and linguistic features of detecting depression sub-challenge dataset. Proceedings of the 9th International On Audio\/Visual Emotion Challenge and Workshop. pp. 47\u201353 (2019)","DOI":"10.1145\/3347320.3357693"},{"key":"359_CR48","doi-asserted-by":"publisher","first-page":"96","DOI":"10.1002\/lio2.354","volume":"5","author":"D Low","year":"2020","unstructured":"Low, D., Bentley, K., Ghosh, S.: Automated assessment of psychiatric disorders using speech: a systematic review. Laryngosc. Investig. Otolaryngol. 5, 96\u2013116 (2020)","journal-title":"Laryngosc. Investig. Otolaryngol."},{"key":"359_CR49","doi-asserted-by":"crossref","unstructured":"Waxer, P.: Therapist training in nonverbal communication: nonverbal cues for depression. Journal Of Clinical Psychology. 30 (1974)","DOI":"10.1002\/1097-4679(197404)30:2<215::AID-JCLP2270300229>3.0.CO;2-Q"},{"key":"359_CR50","unstructured":"Ellgring, H.: Non-verbal communication in depression. (Cambridge University Press, 2007)"},{"key":"359_CR51","unstructured":"Pampouchidou, A.: Automatic detection of visual cues associated with depression. (Universit\u00e9 Bourgogne Franche-Comt\u00e9, 2018)"},{"key":"359_CR52","doi-asserted-by":"crossref","unstructured":"Prasad, T., Naulegari, J.: Investigation of symmetric and asymmetric eye patterns for bell\u2019s palsy diagnosis. 2023 OITS International Conference On Information Technology (OCIT). pp. 655\u2013659 (2023)","DOI":"10.1109\/OCIT59427.2023.10431126"},{"key":"359_CR53","doi-asserted-by":"crossref","unstructured":"Lucas, G., Gratch, J., Scherer, S., Boberg, J., Stratou, G.: Towards an affective interface for assessment of psychological distress. 2015 International Conference On Affective Computing and Intelligent Interaction (ACII). pp. 539\u2013545 (2015)","DOI":"10.1109\/ACII.2015.7344622"},{"key":"359_CR54","doi-asserted-by":"publisher","first-page":"17","DOI":"10.1007\/s12193-014-0161-4","volume":"9","author":"G Stratou","year":"2015","unstructured":"Stratou, G., Scherer, S., Gratch, J., Morency, L.: Automatic nonverbal behavior indicators of depression and ptsd: the effect of gender. J. Multimodal User Interfaces. 9, 17\u201329 (2015)","journal-title":"J. Multimodal User Interfaces."},{"key":"359_CR55","doi-asserted-by":"crossref","unstructured":"Al-gawwam, S., Benaissa, M.: Depression detection from eye blink features. 2018 IEEE International Symposium On Signal Processing And Information Technology (ISSPIT). pp. 388\u2013392 (2018)","DOI":"10.1109\/ISSPIT.2018.8642682"},{"key":"359_CR56","doi-asserted-by":"crossref","unstructured":"McIntyre, G., G\u00f6cke, R., Hyett, M., Green, M., Breakspear, M.: An approach for automatically measuring facial activity in depressed subjects. 2009 3rd International Conference on Affective Computing and Intelligent Interaction And Workshops. pp. 1\u20138 (2009)","DOI":"10.1109\/ACII.2009.5349593"},{"key":"359_CR57","doi-asserted-by":"crossref","unstructured":"Littlewort, G., Whitehill, J., Wu, T., Fasel, I., Frank, M., Movellan, J., Bartlett, M.: The computer expression recognition toolbox (CERT). 2011 IEEE International Conference On Automatic Face & Gesture Recognition (FG). pp. 298\u2013305 (2011)","DOI":"10.1109\/FG.2011.5771414"},{"key":"359_CR58","doi-asserted-by":"crossref","unstructured":"Weber, J., Weber, M., Alcaraz, J.: Depression diagnosis from patient interviews using multimodal machine learning. http:\/\/arxiv.org\/abs\/2508.19390 (2025)","DOI":"10.3389\/fpsyt.2025.1694762"},{"key":"359_CR59","doi-asserted-by":"crossref","unstructured":"Baltrusaitis, T., Zadeh, A., Lim, Y., Morency, L.: Openface 2.0: Facial behavior analysis toolkit. 2018 13th IEEE International Conference On Automatic Face & Gesture Recognition (FG 2018). pp. 59\u201366 (2018)","DOI":"10.1109\/FG.2018.00019"},{"key":"359_CR60","doi-asserted-by":"publisher","first-page":"223","DOI":"10.2466\/pms.1968.26.1.223","volume":"26","author":"R Carver","year":"1968","unstructured":"Carver, R., Winsmann, F.: Effect of high elevation upon physical proficiency, cognitive functioning and subjective symptomatology. Percept. Motor Skills. 26, 223\u2013230 (1968)","journal-title":"Percept. Motor Skills."},{"key":"359_CR61","doi-asserted-by":"publisher","first-page":"489","DOI":"10.1192\/bjp.111.475.489","volume":"111","author":"J Pollitt","year":"1965","unstructured":"Pollitt, J.: Suggestions for a physiological classification of depression. Br. J. Psychiatry 111, 489\u2013495 (1965)","journal-title":"Br. J. Psychiatry"},{"key":"359_CR62","doi-asserted-by":"publisher","first-page":"192","DOI":"10.1016\/j.smhl.2018.07.005","volume":"9","author":"M Boukhechba","year":"2018","unstructured":"Boukhechba, M., Daros, A., Fua, K., Chow, P., Teachman, B., Barnes, L.: DemonicSalmon: monitoring mental health and social interactions of college students using smartphones. Smart Health. 9, 192\u2013203 (2018)","journal-title":"Smart Health."},{"key":"359_CR63","doi-asserted-by":"publisher","first-page":"355","DOI":"10.1109\/TBDATA.2018.2872569","volume":"7","author":"C Yue","year":"2018","unstructured":"Yue, C., Ware, S., Morillo, R., Lu, J., Shang, C., Bi, J., Kamath, J., Russell, A., Bamis, A., Wang, B.: Fusing location data for depression prediction. IEEE Trans. Big Data. 7, 355\u2013370 (2018)","journal-title":"IEEE Trans. Big Data."},{"key":"359_CR64","doi-asserted-by":"publisher","DOI":"10.2196\/24365","volume":"9","author":"R Bai","year":"2021","unstructured":"Bai, R., Xiao, L., Guo, Y., Zhu, X., Li, N., Wang, Y., Chen, Q., Feng, L., Wang, Y., Yu, X., et al.: Tracking and monitoring mood stability of patients with major depressive disorder by machine learning models using passive digital data: prospective naturalistic multicenter study. JMIR MHealth UHealth. 9, e24365 (2021)","journal-title":"JMIR MHealth UHealth."},{"key":"359_CR65","doi-asserted-by":"publisher","DOI":"10.2196\/26540","volume":"9","author":"K Opoku Asare","year":"2021","unstructured":"Opoku Asare, K., Terhorst, Y., Vega, J., Peltonen, E., Lagerspetz, E., Ferreira, D.: Predicting depression from smartphone behavioral markers using machine learning methods, hyperparameter optimization, and feature importance analysis: exploratory study. JMIR MHealth UHealth. 9, e26540 (2021)","journal-title":"JMIR MHealth UHealth."},{"key":"359_CR66","doi-asserted-by":"publisher","first-page":"631","DOI":"10.1176\/appi.ajp.2018.17111194","volume":"175","author":"F Schuch","year":"2018","unstructured":"Schuch, F., Vancampfort, D., Firth, J., Rosenbaum, S., Ward, P., Silva, E., Hallgren, M., Ponce De Leon, A., Dunn, A., Deslandes, A., et al.: Physical activity and incident depression: a meta-analysis of prospective cohort studies. Am. J. Psychiatry 175, 631\u2013648 (2018)","journal-title":"Am. J. Psychiatry"},{"key":"359_CR67","doi-asserted-by":"publisher","first-page":"1053","DOI":"10.1109\/JBHI.2015.2446195","volume":"20","author":"E Garcia-Ceja","year":"2015","unstructured":"Garcia-Ceja, E., Osmani, V., Mayora, O.: Automatic stress detection in working environments from smartphones\u2019 accelerometer data: a first step. IEEE J. Biomed. Health Inform. 20, 1053\u20131060 (2015)","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"359_CR68","doi-asserted-by":"crossref","unstructured":"Hao, T., Xing, G., Zhou, G.: isleep: Unobtrusive sleep quality monitoring using smartphones. Proceedings Of The 11th ACM Conference On Embedded Networked Sensor Systems. pp. 1\u201314 (2013)","DOI":"10.1145\/2517351.2517359"},{"key":"359_CR69","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/1471-2458-11-66","volume":"11","author":"S Thom\u00e9e","year":"2011","unstructured":"Thom\u00e9e, S., H\u00e4renstam, A., Hagberg, M.: Mobile phone use and stress, sleep disturbances, and symptoms of depression among young adults-a prospective cohort study. BMC Public Health 11, 1\u201311 (2011)","journal-title":"BMC Public Health"},{"key":"359_CR70","doi-asserted-by":"crossref","unstructured":"Alvarez-Lozano, J., Osmani, V., Mayora, O., Frost, M., Bardram, J., Faurholt-Jepsen, M., Kessing, L.: Tell me your apps and I will tell you your mood: correlation of apps usage with bipolar disorder state. Proceedings Of The 7th International Conference On Pervasive Technologies Related To Assistive Environments. pp. 1\u20137 (2014)","DOI":"10.1145\/2674396.2674408"},{"key":"359_CR71","doi-asserted-by":"crossref","unstructured":"Van, N., Daril, M., Ali, M., Korejo, M.: Enhancing Psychological Well-being in Higher Education Post-Covid-19 Pandemic. The Role of AI-Based Support Systems\u2013Bibliometric Reviews. International Journal Of Online And Biomedical Engineering (iJOE). 20, pp. 139\u2013152 (2024), https:\/\/online-journals.org\/index.php\/i-joe\/article\/view\/48001","DOI":"10.3991\/ijoe.v20i06.48001"},{"key":"359_CR72","doi-asserted-by":"crossref","unstructured":"Xiong, H., Huang, Y., Barnes, L., Gerber, M.: Sensus: a cross-platform, general-purpose system for mobile crowdsensing in human-subject studies. Proceedings Of The 2016 ACM International Joint Conference On Pervasive And Ubiquitous Computing. pp. 415\u2013426 (2016)","DOI":"10.1145\/2971648.2971711"},{"key":"359_CR73","doi-asserted-by":"crossref","unstructured":"Morales, M., Scherer, S., Levitan, R.: A cross-modal review of indicators for depression detection systems. Proceedings of the Fourth Workshop on Computational Linguistics and Clinical Psychology\u2013from Linguistic Signal To Clinical Reality. pp. 1\u201312 (2017)","DOI":"10.18653\/v1\/W17-3101"},{"key":"359_CR74","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2023.107702","volume":"240","author":"H Zhang","year":"2023","unstructured":"Zhang, H., Wang, H., Han, S., Li, W., Zhuang, L.: Detecting depression tendency with multimodal features. Comput. Methods Programs Biomed. 240, 107702 (2023). https:\/\/doi.org\/10.1016\/j.cmpb.2023.107702","journal-title":"Comput. Methods Programs Biomed."},{"key":"359_CR75","doi-asserted-by":"crossref","unstructured":"Cohn, J., Cummins, N., Epps, J., Goecke, R., Joshi, J., Scherer, S.: Multimodal assessment of depression from behavioral signals. The Handbook Of Multimodal-Multisensor Interfaces: Foundations, User Modeling, And Common Modality Combinations - 2, 375\u2013417 (2018)","DOI":"10.1145\/3107990.3108004"},{"key":"359_CR76","doi-asserted-by":"publisher","DOI":"10.1093\/oso\/9780195169157.001.0001","volume-title":"Handbook of emotion elicitation and assessment","author":"J Coan","year":"2007","unstructured":"Coan, J., Allen, J.: Handbook of emotion elicitation and assessment. Oxford University Press, Oxford (2007)"},{"key":"359_CR77","doi-asserted-by":"publisher","first-page":"180","DOI":"10.3389\/fpsyg.2016.00180","volume":"7","author":"M Uhrig","year":"2016","unstructured":"Uhrig, M., Trautmann, N., Baumg\u00e4rtner, U., Treede, R., Henrich, F., Hiller, W., Marschall, S.: Emotion elicitation: a comparison of pictures and films. Front. Psychol. 7, 180 (2016)","journal-title":"Front. Psychol."},{"key":"359_CR78","unstructured":"Lang, P., Bradley, M., Cuthbert, B., et al.: International affective picture system (IAPS): Technical manual and affective ratings. NIMH Center For The Study Of Emotion And Attention. 1, 3 (1997)"},{"key":"359_CR79","doi-asserted-by":"crossref","unstructured":"Ko\u0142akowska, A.: A review of emotion recognition methods based on keystroke dynamics and mouse movements. 2013 6th International Conference On Human System Interactions (HSI). pp. 548\u2013555 (2013)","DOI":"10.1109\/HSI.2013.6577879"},{"key":"359_CR80","first-page":"503","volume":"2016","author":"P Barbosa","year":"2016","unstructured":"Barbosa, P., Madureira, S.: Elicitation techniques for cross-linguistic research on professional and non-professional speaking styles. Proc. Speech Prosody 2016, 503\u2013507 (2016)","journal-title":"Proc. Speech Prosody"},{"key":"359_CR81","unstructured":"Gratch, J., Artstein, R., Lucas, G., Stratou, G., Scherer, S., Nazarian, A., Wood, R., Boberg, J., DeVault, D., Marsella, S., et al.: The distress analysis interview corpus of human and computer interviews. LREC. pp. 3123-3128 (2014)"},{"key":"359_CR82","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.: Detecting depression severity from vocal prosody. IEEE Trans. Affect. Comput. 4, 142\u2013150 (2012)","journal-title":"IEEE Trans. Affect. Comput."},{"key":"359_CR83","doi-asserted-by":"crossref","unstructured":"Ringeval, F., Schuller, B., Valstar, M., Cummins, N., Cowie, R., Tavabi, L., Schmitt, M., Alisamir, S., Amiriparian, S., Messner, E., et al.: AVEC 2019 workshop and challenge: state-of-mind, detecting depression with AI, and cross-cultural affect recognition. Proceedings Of The 9th International On Audio\/visual Emotion Challenge And Workshop. pp. 3\u201312 (2019)","DOI":"10.1145\/3347320.3357688"},{"key":"359_CR84","doi-asserted-by":"publisher","first-page":"1557","DOI":"10.1109\/TASL.2006.878256","volume":"14","author":"S Tranter","year":"2006","unstructured":"Tranter, S., Reynolds, D.: An overview of automatic speaker diarization systems. IEEE Trans. Audio, Speech, Lang. Process. 14, 1557\u20131565 (2006)","journal-title":"IEEE Trans. Audio, Speech, Lang. Process."},{"key":"359_CR85","doi-asserted-by":"crossref","unstructured":"Satapathi, A., Mishra, A.: Build a Desktop Application for Speech-to-Text Conversation Using Azure Cognitive Services. Developing Cloud-Native Solutions With Microsoft Azure And NET : Build Highly Scalable Solutions For The Enterprise. pp. 219\u2013230 (2023)","DOI":"10.1007\/978-1-4842-9004-0_9"},{"key":"359_CR86","doi-asserted-by":"publisher","first-page":"11","DOI":"10.33736\/jita.2815.2021","volume":"9","author":"H Wang","year":"2021","unstructured":"Wang, H.: Speech recorder and translator using google cloud speech-to-text and translation. J. IT Asia. 9, 11\u201328 (2021)","journal-title":"J. IT Asia."},{"key":"359_CR87","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s13640-018-0324-4","volume":"2018","author":"B Johnston","year":"2018","unstructured":"Johnston, B., Chazal, P.: A review of image-based automatic facial landmark identification techniques. EURASIP J. Image Video Process. 2018, 1\u201323 (2018)","journal-title":"EURASIP J. Image Video Process."},{"key":"359_CR88","doi-asserted-by":"publisher","first-page":"228","DOI":"10.1016\/j.jvcir.2018.11.003","volume":"57","author":"Q Wang","year":"2018","unstructured":"Wang, Q., Yang, H., Yu, Y.: Facial expression video analysis for depression detection in Chinese patients. J. Visual Commun. Image Represent. 57, 228\u2013233 (2018)","journal-title":"J. Visual Commun. Image Represent."},{"key":"359_CR89","doi-asserted-by":"crossref","unstructured":"Dibeklio\u011flu, H., Hammal, Z., Yang, Y., Cohn, J.: Multimodal detection of depression in clinical interviews. Proceedings Of The 2015 ACM On International Conference On Multimodal Interaction. pp. 307\u2013310 (2015)","DOI":"10.1145\/2818346.2820776"},{"key":"359_CR90","doi-asserted-by":"crossref","unstructured":"Guohou, S., Lina, Z., Dongsong, Z.: What reveals about depression level, The role of multimodal features at the level of interview questions. 103349 (2020)","DOI":"10.1016\/j.im.2020.103349"},{"key":"359_CR91","doi-asserted-by":"crossref","unstructured":"Williamson, J., Quatieri, T., Helfer, B., Ciccarelli, G., Mehta, D.: Vocal and facial biomarkers of depression based on motor incoordination and timing. Proceedings Of The 4th International Workshop On Audio\/visual Emotion Challenge. pp. 65\u201372 (2014)","DOI":"10.1145\/2661806.2661809"},{"key":"359_CR92","doi-asserted-by":"crossref","unstructured":"Bersani, G., Polli, E., Valeriani, G., Zullo, D., Melcore, C., Capra, E., Quartini, A., Marino, P., Minichino, A., Bernabei, L., et al.: Facial expression in patients with bipolar disorder and schizophrenia in response to emotional stimuli: a partially shared cognitive and social deficit of the two disorders. Neuropsychiatric Disease And Treatment. pp. 1137\u20131144 (2013)","DOI":"10.2147\/NDT.S46525"},{"key":"359_CR93","doi-asserted-by":"publisher","first-page":"123","DOI":"10.1007\/BF01450852","volume":"15","author":"D DeMenthon","year":"1995","unstructured":"DeMenthon, D., Davis, L.: Model-based object pose in 25 lines of code. Int. J. Comput. Vision 15, 123\u2013141 (1995)","journal-title":"Int. J. Comput. Vision"},{"key":"359_CR94","doi-asserted-by":"crossref","unstructured":"Alghowinem, S., Goecke, R., Wagner, M., Parkerx, G., Breakspear, M.: Head pose and movement analysis as an indicator of depression. 2013 Humaine Association Conference on Affective Computing and Intelligent Interaction. pp. 283\u2013288 (2013)","DOI":"10.1109\/ACII.2013.53"},{"key":"359_CR95","doi-asserted-by":"publisher","first-page":"648","DOI":"10.1016\/j.imavis.2014.06.001","volume":"32","author":"S Scherer","year":"2014","unstructured":"Scherer, S., Stratou, G., Lucas, G., Mahmoud, M., Boberg, J., Gratch, J., Morency, L., et al.: Automatic audiovisual behavior descriptors for psychological disorder analysis. Image Vision Comput. 32, 648\u2013658 (2014)","journal-title":"Image Vision Comput."},{"key":"359_CR96","doi-asserted-by":"publisher","first-page":"445","DOI":"10.1109\/TAFFC.2017.2724035","volume":"10","author":"A Pampouchidou","year":"2017","unstructured":"Pampouchidou, A., Simos, P., Marias, K., Meriaudeau, F., Yang, F., Pediaditis, M., Tsiknakis, M.: Automatic assessment of depression based on visual cues: A systematic review. IEEE Trans. Affect. Comput. 10, 445\u2013470 (2017)","journal-title":"IEEE Trans. Affect. Comput."},{"key":"359_CR97","doi-asserted-by":"publisher","first-page":"217","DOI":"10.1007\/s12193-013-0123-2","volume":"7","author":"J Joshi","year":"2013","unstructured":"Joshi, J., Goecke, R., Alghowinem, S., Dhall, A., Wagner, M., Epps, J., Parker, G., Breakspear, M.: Multimodal assistive technologies for depression diagnosis and monitoring. J. Multimodal User Interfaces. 7, 217\u2013228 (2013)","journal-title":"J. Multimodal User Interfaces."},{"key":"359_CR98","doi-asserted-by":"crossref","unstructured":"Joshi, J., Dhall, A., Goecke, R., Cohn, J.: Relative body parts movement for automatic depression analysis. 2013 Humaine Association Conference on Affective Computing and Intelligent Interaction. pp. 492\u2013497 (2013)","DOI":"10.1109\/ACII.2013.87"},{"key":"359_CR99","doi-asserted-by":"crossref","unstructured":"Almaev, T., Valstar, M.: Local gabor binary patterns from three orthogonal planes for automatic facial expression recognition. 2013 Humaine Association Conference on Affective Computing and Intelligent Interaction. pp. 356\u2013361 (2013)","DOI":"10.1109\/ACII.2013.65"},{"key":"359_CR100","doi-asserted-by":"crossref","unstructured":"Pampouchidou, A., Marias, K., Tsiknakis, M., Simos, P., Yang, F., Lema, G., Meriaudeau, F.: Video-based depression detection using local curvelet binary patterns in pairwise orthogonal planes. 2016 38th Annual International Conference Of The IEEE Engineering In Medicine And Biology Society (EMBC). pp. 3835\u20133838 (2016)","DOI":"10.1109\/EMBC.2016.7591564"},{"key":"359_CR101","doi-asserted-by":"crossref","unstructured":"Pampouchidou, A., Marias, K., Tsiknakis, M., Simos, P., Yang, F., Meriaudeau, F.: Designing a framework for assisting depression severity assessment from facial image analysis. 2015 IEEE International Conference on Signal and Image Processing Applications (ICSIPA). pp. 578\u2013583 (2015)","DOI":"10.1109\/ICSIPA.2015.7412257"},{"key":"359_CR102","doi-asserted-by":"publisher","first-page":"1809","DOI":"10.3389\/fpsyg.2015.01809","volume":"6","author":"N Carvalho","year":"2015","unstructured":"Carvalho, N., Laurent, E., Noiret, N., Chopard, G., Haffen, E., Bennabi, D., Vandel, P.: Eye movement in unipolar and bipolar depression: a systematic review of the literature. Front. Psychol. 6, 1809 (2015)","journal-title":"Front. Psychol."},{"key":"359_CR103","doi-asserted-by":"crossref","unstructured":"Alghowinem, S., Goecke, R., Wagner, M., Parker, G., Breakspear, M.: Eye movement analysis for depression detection. 2013 IEEE International Conference On Image Processing. pp. 4220\u20134224 (2013)","DOI":"10.1109\/ICIP.2013.6738869"},{"key":"359_CR104","unstructured":"Alghowinem, S., Gedeon, T., Goecke, R., Cohn, J., Parker, G.: Interpretation of depression detection models via feature selection methods. IEEE Transactions on Affective Computing. (2020)"},{"key":"359_CR105","doi-asserted-by":"crossref","unstructured":"Pan, Z., Ma, H., Zhang, L., Wang, Y.: Depression detection based on reaction time and eye movement. 2019 IEEE International Conference On Image Processing (ICIP). pp. 2184\u20132188 (2019)","DOI":"10.1109\/ICIP.2019.8803181"},{"key":"359_CR106","doi-asserted-by":"crossref","unstructured":"Cohn, J., Kruez, T., Matthews, I., Yang, Y., Nguyen, M., Padilla, M., Zhou, F., Torre, F.: Detecting depression from facial actions and vocal prosody. 2009 3rd International Conference on Affective Computing and Intelligent Interaction and Workshops. pp. 1\u20137 (2009)","DOI":"10.1109\/ACII.2009.5349358"},{"key":"359_CR107","doi-asserted-by":"crossref","unstructured":"Syed, Z., Sidorov, K., Marshall, D.: Depression severity prediction based on biomarkers of psychomotor retardation. Proceedings of the 7th Annual Workshop on Audio\/Visual Emotion Challenge. pp. 37\u201343 (2017)","DOI":"10.1145\/3133944.3133947"},{"key":"359_CR108","unstructured":"Stasak, B.: An Investigation of Acoustic , Linguistic , and Affect Based Methods for Speech D epression Assessment. (2018)"},{"key":"359_CR109","doi-asserted-by":"crossref","unstructured":"Smirnova, D., Cumming, P., Sloeva, E., Kuvshinova, N., Romanov, D., Nosachev, G.: Language patterns discriminate mild depression from normal sadness and euthymic state. Frontiers Psychiatry. 9 (2018)","DOI":"10.3389\/fpsyt.2018.00105"},{"key":"359_CR110","doi-asserted-by":"crossref","unstructured":"Pampouchidou, A., Simantiraki, O., Fazlollahi, A., Pediaditis, M., Manousos, D., Roniotis, A., Giannakakis, G., Meriaudeau, F., Simos, P., Marias, K.: Depression assessment by fusing high and low level features from audio, video, and text. Proceedings of the 6th International Workshop on Audio\/Visual Emotion Challenge. pp. 27-34 (2016)","DOI":"10.1145\/2988257.2988266"},{"key":"359_CR111","doi-asserted-by":"publisher","first-page":"43","DOI":"10.1016\/j.cobeha.2017.07.005","volume":"18","author":"S Guntuku","year":"2017","unstructured":"Guntuku, S., Yaden, D., Kern, M., Ungar, L., Eichstaedt, J.: Detecting depression and mental illness on social media: an integrative review. Curr. Opin. Behav. Sci. 18, 43\u201349 (2017). https:\/\/doi.org\/10.1016\/j.cobeha.2017.07.005","journal-title":"Curr. Opin. Behav. Sci."},{"key":"359_CR112","doi-asserted-by":"crossref","unstructured":"Dang, T., Atcheson, M., Stasak, B., Hayat, M., Goecke, R., Huang, Z., Le, P., Epps, J., Jayawardena, S., Sethu, V.: Investigating word affect features and fusion of probabilistic predictions incorporating uncertainty in AVEC 2017. AVEC 2017 - Proceedings Of The 7th Annual Workshop On Audio\/Visual Emotion Challenge, Co-located With MM 2017. pp. 27\u201335 (2017)","DOI":"10.1145\/3133944.3133952"},{"key":"359_CR113","doi-asserted-by":"publisher","first-page":"7","DOI":"10.1192\/bjp.139.1.7","volume":"139","author":"T Manschreck","year":"1981","unstructured":"Manschreck, T., Maher, B., Ader, D.: Formal thought disorder, the type-token ratio, and disturbed voluntary motor movement in schizophrenia. Br. J. Psychiatry 139, 7\u201315 (1981)","journal-title":"Br. J. Psychiatry"},{"key":"359_CR114","unstructured":"Nadeem, M.: Identifying Depression on Twitter. http:\/\/arxiv.org\/abs\/1607.07384 (2016)"},{"key":"359_CR115","doi-asserted-by":"publisher","first-page":"757","DOI":"10.1002\/tesq.194","volume":"49","author":"K Kyle","year":"2015","unstructured":"Kyle, K., Crossley, S.: Automatically assessing lexical sophistication: indices, tools, findings, and application. TESOL Q. 49, 757\u2013786 (2015). https:\/\/doi.org\/10.1002\/tesq.194","journal-title":"TESOL Q."},{"key":"359_CR116","unstructured":"Crossley, S., Kyle, K., Davenport, J., McNamara, D.: Automatic assessment of constructed response data in a chemistry tutor. International Educational Data Mining Society. (2016)"},{"key":"359_CR117","doi-asserted-by":"publisher","first-page":"511","DOI":"10.1080\/0163853X.2014.910723","volume":"51","author":"SA Crossley","year":"2014","unstructured":"Crossley, S.A., McNamara, D.: Analyzing discourse processing using a simple natural language processing tool. Discourse Process. 51, 511\u2013534 (2014)","journal-title":"Discourse Process."},{"key":"359_CR118","doi-asserted-by":"crossref","unstructured":"V\u00e1zquez-Romero, A., Gallardo-Antol\u00edn, A.: Automatic detection of depression in speech using ensemble convolutional neural networks. Entropy. 22 (2020)","DOI":"10.3390\/e22060688"},{"key":"359_CR119","doi-asserted-by":"publisher","DOI":"10.1016\/j.im.2020.103349","volume":"57","author":"S Guohou","year":"2020","unstructured":"Guohou, S., Lina, Z., Dongsong, Z.: What reveals about depression level? The role of multimodal features at the level of interview questions. Inf. Manag. 57, 103349 (2020). https:\/\/doi.org\/10.1016\/j.im.2020.103349","journal-title":"Inf. Manag."},{"key":"359_CR120","doi-asserted-by":"publisher","unstructured":"American, S., America, N., American, S.: Acoustic Methods in Psychiatry Author (s): Peter F . Ostwald Published by: Scientific American, a division of Nature America, Inc. Stable https:\/\/doi.org\/10.2307\/24931814 (1965)","DOI":"10.2307\/24931814"},{"key":"359_CR121","doi-asserted-by":"crossref","unstructured":"Lech, M.: Detection of Adolescent Depression from Speech Using Optimised Spectral Roll-Off Parameters. (2018)","DOI":"10.26717\/BJSTR.2018.05.0001156"},{"key":"359_CR122","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2022.106122","volume":"150","author":"B Kaur","year":"2022","unstructured":"Kaur, B., Rathi, S., Agrawal, R.: Enhanced depression detection from speech using Quantum Whale Optimization Algorithm for feature selection. Comput. Biol. Med. 150, 106122 (2022). https:\/\/doi.org\/10.1016\/j.compbiomed.2022.106122","journal-title":"Comput. Biol. Med."},{"key":"359_CR123","doi-asserted-by":"publisher","DOI":"10.1016\/j.jvoice.2021.06.018","author":"W Silva","year":"2021","unstructured":"Silva, W., Lopes, L., Galdino, M., Almeida, A.: Voice acoustic parameters as predictors of depression. J. Voice (2021). https:\/\/doi.org\/10.1016\/j.jvoice.2021.06.018","journal-title":"J. Voice"},{"key":"359_CR124","doi-asserted-by":"publisher","first-page":"96","DOI":"10.1109\/TBME.2007.900562","volume":"55","author":"E Moore","year":"2008","unstructured":"Moore, E., Clements, M., Peifer, J., Weisser, L.: Critical analysis of the impact of glottal features in the classification of clinical depression in speech. IEEE Trans. Biomed. Eng. 55, 96\u2013107 (2008)","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"359_CR125","doi-asserted-by":"publisher","first-page":"192","DOI":"10.1016\/j.smhl.2018.07.005","volume":"9\u201310","author":"M Boukhechba","year":"2018","unstructured":"Boukhechba, M., Daros, A., Fua, K., Chow, P., Teachman, B., Barnes, L.: DemonicSalmon: monitoring mental health and social interactions of college students using smartphones. Smart Health. 9\u201310, 192\u2013203 (2018). https:\/\/doi.org\/10.1016\/j.smhl.2018.07.005","journal-title":"Smart Health."},{"key":"359_CR126","doi-asserted-by":"crossref","unstructured":"Yamamoto, N., Ochiai, K., Inagaki, A., Fukazawa, Y., Kimoto, M., Kiriu, K., Kaminishi, K., Ota, J., Okimura, T., Terasawa, Y., et al.: Physiological stress level estimation based on smartphone logs. 2018 Eleventh International Conference On Mobile Computing And Ubiquitous Network (ICMU). pp. 1\u20136 (2018)","DOI":"10.23919\/ICMU.2018.8653590"},{"key":"359_CR127","doi-asserted-by":"publisher","first-page":"1750","DOI":"10.1109\/JBHI.2017.2649602","volume":"21","author":"D Kelly","year":"2017","unstructured":"Kelly, D., Curran, K., Caulfield, B.: Automatic prediction of health status using smartphone-derived behavior profiles. IEEE J. Biomed. Health Inform. 21, 1750\u20131760 (2017)","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"359_CR128","doi-asserted-by":"publisher","DOI":"10.1016\/j.smhl.2019.100093","volume":"15","author":"S Ware","year":"2020","unstructured":"Ware, S., Yue, C., Morillo, R., Lu, J., Shang, C., Bi, J., Kamath, J., Russell, A., Bamis, A., Wang, B.: Predicting depressive symptoms using smartphone data. Smart Health. 15, 100093 (2020). https:\/\/doi.org\/10.1016\/j.smhl.2019.100093","journal-title":"Smart Health."},{"key":"359_CR129","doi-asserted-by":"publisher","first-page":"1053","DOI":"10.1109\/JBHI.2015.2446195","volume":"20","author":"E Garcia-Ceja","year":"2016","unstructured":"Garcia-Ceja, E., Osmani, V., Mayora, O.: Automatic stress detection in working environments from smartphones\u2019 accelerometer data: a first step. IEEE J. Biomed. Health Inform. 20, 1053\u20131060 (2016)","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"359_CR130","doi-asserted-by":"publisher","DOI":"10.1016\/j.paid.2022.111592","volume":"192","author":"M Jokela","year":"2022","unstructured":"Jokela, M.: Why is cognitive ability associated with psychological distress and wellbeing? Exploring psychological, biological, and social mechanisms. Personal. Ind. Differ. 192, 111592 (2022). https:\/\/doi.org\/10.1016\/j.paid.2022.111592","journal-title":"Personal. Ind. Differ."},{"key":"359_CR131","doi-asserted-by":"publisher","first-page":"1303","DOI":"10.1017\/S0033291712002139","volume":"43","author":"A Ali","year":"2013","unstructured":"Ali, A., Ambler, G., Strydom, A., Rai, D., Cooper, C., McManus, S., Weich, S., Meltzer, H., Dein, S., Hassiotis, A.: The relationship between happiness and intelligent quotient: the contribution of socio-economic and clinical factors. Psychol. Med. 43, 1303\u20131312 (2013)","journal-title":"Psychol. Med."},{"key":"359_CR132","doi-asserted-by":"publisher","DOI":"10.1016\/j.ibmed.2025.100223","volume":"11","author":"K Murugavel","year":"2025","unstructured":"Murugavel, K., Parthasarathy, R., Mathivanan, S., Srinivasan, S., Shivahare, B., Shah, M.: A multimodal machine learning model for bipolar disorder mania classification: Insights from acoustic, linguistic, and visual cues. Intell.-Based Med. 11, 100223 (2025). https:\/\/doi.org\/10.1016\/j.ibmed.2025.100223","journal-title":"Intell.-Based Med."},{"key":"359_CR133","doi-asserted-by":"publisher","DOI":"10.1016\/j.jbi.2019.103151","volume":"93","author":"Y Fukazawa","year":"2019","unstructured":"Fukazawa, Y., Ito, T., Okimura, T., Yamashita, Y., Maeda, T., Ota, J.: Predicting anxiety state using smartphone-based passive sensing. J. Biomed. Inform. 93, 103151 (2019). https:\/\/doi.org\/10.1016\/j.jbi.2019.103151","journal-title":"J. Biomed. Inform."},{"key":"359_CR134","doi-asserted-by":"crossref","unstructured":"Obuchi, M., Huckins, J., Wang, W., Dasilva, A., Rogers, C., Murphy, E., Hedlund, E., Holtzheimer, P., Mirjafari, S., Campbell, A.: Predicting brain functional connectivity using mobile sensing. Proceedings Of The ACM On Interactive, Mobile, Wearable And Ubiquitous Technologies. 4, 1\u201322 (2020)","DOI":"10.1145\/3381001"},{"key":"359_CR135","doi-asserted-by":"publisher","first-page":"323","DOI":"10.1002\/da.22466","volume":"33","author":"L Lin","year":"2016","unstructured":"Lin, L., Sidani, J., Shensa, A., Radovic, A., Miller, E., Colditz, J., Hoffman, B., Giles, L., Primack, B.: Association between social media use and depression among US young adults. Depress. Anxiety. 33, 323\u2013331 (2016)","journal-title":"Depress. Anxiety."},{"key":"359_CR136","first-page":"1","volume":"9","author":"K Asare","year":"2021","unstructured":"Asare, K., Terhorst, Y., Vega, J., Peltonen, E., Lagerspetz, E., Ferreira, D.: Predicting depression from smartphone behavioral markers using machine learning methods, hyperparameter optimization, and feature importance analysis: Exploratory study. JMIR MHealth UHealth. 9, 1\u201317 (2021)","journal-title":"JMIR MHealth UHealth."},{"key":"359_CR137","doi-asserted-by":"publisher","first-page":"51","DOI":"10.1109\/TAFFC.2016.2592504","volume":"9","author":"M Ciman","year":"2018","unstructured":"Ciman, M., Wac, K.: Individuals\u2019 Stress Assessment Using Human-Smartphone Interaction Analysis. IEEE Trans. Affect. Comput. 9, 51\u201365 (2018)","journal-title":"IEEE Trans. Affect. Comput."},{"key":"359_CR138","doi-asserted-by":"publisher","first-page":"910","DOI":"10.1016\/j.bbe.2020.04.005","volume":"40","author":"R Nawaz","year":"2020","unstructured":"Nawaz, R., Cheah, K., Nisar, H., Yap, V.: Comparison of different feature extraction methods for EEG-based emotion recognition. Biocybern. Biomed. Eng. 40, 910\u2013926 (2020). https:\/\/doi.org\/10.1016\/j.bbe.2020.04.005","journal-title":"Biocybern. Biomed. Eng."},{"key":"359_CR139","unstructured":"Alghowinem, S., Gedeon, T., Goecke, R., Cohn, J., Parker, G.: Interpretation of depression detection models via feature selection methods. IEEE Transactions On Affective Computing. (2020)"},{"key":"359_CR140","doi-asserted-by":"publisher","first-page":"17703","DOI":"10.1007\/s11042-022-12420-2","volume":"81","author":"S Rathi","year":"2022","unstructured":"Rathi, S., Kaur, B., Agrawal, R.: Selection of relevant visual feature sets for enhanced depression detection using incremental linear discriminant analysis. Multimed. Tools Appl. 81, 17703\u201317727 (2022)","journal-title":"Multimed. Tools Appl."},{"key":"359_CR141","unstructured":"Hall, M., Smith, L.: Feature selection for machine learning: comparing a correlation-based filter approach to the wrapper. Proceedings of the Twelfth International Florida Artificial Intelligence Research Society Conference. pp. 235\u2013239 (1999)"},{"key":"359_CR142","doi-asserted-by":"publisher","first-page":"1226","DOI":"10.1109\/TPAMI.2005.159","volume":"27","author":"H Peng","year":"2005","unstructured":"Peng, H., Long, F., Ding, C.: Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy. IEEE Trans. Pattern Anal. Mach. Intell. 27, 1226\u20131238 (2005)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"359_CR143","unstructured":"Yang, H., Moody, J.: Data visualization and feature selection: new algorithms for nongaussian data. Advances In Neural Information Processing Systems. 12 (1999)"},{"key":"359_CR144","doi-asserted-by":"publisher","first-page":"44","DOI":"10.1109\/5254.671091","volume":"13","author":"J Yang","year":"1998","unstructured":"Yang, J., Honavar, V.: Feature subset selection using a genetic algorithm. IEEE Intell. Syst. Appl. 13, 44\u201349 (1998)","journal-title":"IEEE Intell. Syst. Appl."},{"key":"359_CR145","first-page":"1357","volume":"3","author":"A Rakotomamonjy","year":"2003","unstructured":"Rakotomamonjy, A.: Variable selection using SVM-based criteria. J. Mach. Learn. Res. 3, 1357\u20131370 (2003)","journal-title":"J. Mach. Learn. Res."},{"key":"359_CR146","doi-asserted-by":"crossref","unstructured":"Kacem, A., Hammal, Z., Daoudi, M., Cohn, J.: Detecting depression severity by interpretable representations of motion dynamics. pp. 739\u2013745 (2018)","DOI":"10.1109\/FG.2018.00116"},{"key":"359_CR147","doi-asserted-by":"crossref","unstructured":"Maridaki, A., Pampouchidou, A., Marias, K., Tsiknakis, M.: Machine learning techniques for automatic depression assessment. 2018 41st International Conference on Telecommunications and Signal Processing (TSP). pp. 1\u20135 (2018)","DOI":"10.1109\/TSP.2018.8441422"},{"key":"359_CR148","doi-asserted-by":"crossref","unstructured":"Li, M., Cao, L., Zhai, Q., Li, P., Liu, S., Li, R., Feng, L., Wang, G., Hu, B., Lu, S.: Method of Depression Classification Based on Behavioral and Physiological Signals of Eye Movement. Complexity. 2020 (2020)","DOI":"10.1155\/2020\/4174857"},{"key":"359_CR149","doi-asserted-by":"publisher","first-page":"217","DOI":"10.1016\/j.matpr.2022.01.467","volume":"58","author":"M Joshi","year":"2022","unstructured":"Joshi, M., Kanoongo, N.: Depression detection using emotional artificial intelligence and machine learning: a closer review. Mater. Today: Proc. 58, 217\u2013226 (2022). https:\/\/doi.org\/10.1016\/j.matpr.2022.01.467","journal-title":"Mater. Today: Proc."},{"key":"359_CR150","unstructured":"Pampouchidou, A.: Automatic detection of visual cues associated with depression to cite this version : HAL Id : tel-02122342 Doctoral Thesis Automatic Detection of Visual Cues Associated to Depression Anastasia Pampouchidou. (2019)"},{"key":"359_CR151","doi-asserted-by":"publisher","first-page":"592","DOI":"10.1192\/bjp.129.6.592","volume":"129","author":"E Szabadi","year":"1976","unstructured":"Szabadi, E., Bradshaw, C., Besson, J.: Elongation of pause-time in speech: a simple, objective measure of motor retardation in depression. Br. J. Psychiatry 129, 592\u2013597 (1976)","journal-title":"Br. J. Psychiatry"},{"key":"359_CR152","doi-asserted-by":"publisher","first-page":"4787","DOI":"10.1007\/s11042-022-12315-2","volume":"82","author":"RP Thati","year":"2023","unstructured":"Thati, R.P., Dhadwal, A.S., Kumar, P., et al.: A novel multi-modal depression detection approach based on mobile crowd sensing and task-based mechanisms. Multimed. Tools Appl. 82, 4787\u20134820 (2023). https:\/\/doi.org\/10.1007\/s11042-022-12315-2","journal-title":"Multimed. Tools Appl."},{"key":"359_CR153","doi-asserted-by":"crossref","unstructured":"Aleem, S., Huda, N., Amin, R., Khalid, S., Alshamrani, S., Alshehri, A.: Machine Learning Algorithms for Depression, Diagnosis, Electronics (2022)","DOI":"10.3390\/electronics11071111"},{"key":"359_CR154","doi-asserted-by":"publisher","first-page":"895","DOI":"10.1007\/s12652-016-0395-y","volume":"8","author":"T Yang","year":"2017","unstructured":"Yang, T., Wu, C., Huang, K., Su, M.: Coupled HMM-based multimodal fusion for mood disorder detection through elicited audio-visual signals. J. Ambient Intell. Humaniz. Comput. 8, 895\u2013906 (2017)","journal-title":"J. Ambient Intell. Humaniz. Comput."},{"key":"359_CR155","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2021.104499","volume":"135","author":"R Chiong","year":"2021","unstructured":"Chiong, R., Budhi, G., Dhakal, S., Chiong, F.: A textual-based featuring approach for depression detection using machine learning classifiers and social media texts. Comput. Biol. Med. 135, 104499 (2021). https:\/\/doi.org\/10.1016\/j.compbiomed.2021.104499","journal-title":"Comput. Biol. Med."},{"key":"359_CR156","doi-asserted-by":"publisher","first-page":"342","DOI":"10.1016\/j.icte.2020.11.005","volume":"7","author":"E Alshdaifat","year":"2021","unstructured":"Alshdaifat, E., Al-hassan, M., Aloqaily, A.: Effective heterogeneous ensemble classification: An alternative approach for selecting base classifiers. ICT Express. 7, 342\u2013349 (2021). https:\/\/doi.org\/10.1016\/j.icte.2020.11.005","journal-title":"ICT Express."},{"key":"359_CR157","doi-asserted-by":"crossref","unstructured":"Vazquez-Romero, A., Gallardo-Antolin, A.: Automatic detection of depression in speech using ensemble convolutional neural networks. Entropy. 22 (2020)","DOI":"10.3390\/e22060688"},{"key":"359_CR158","doi-asserted-by":"crossref","unstructured":"Cohn, J., Kruez, T., Matthews, I., Yang, Y., Nguyen, M., Padilla, M., Zhou, F., De La Torre, F.: Detecting depression from facial actions and vocal prosody. Proceedings - 2009 3rd International Conference on Affective Computing and Intelligent Interaction and Workshops, ACII 2009. (2009)","DOI":"10.1109\/ACII.2009.5349358"},{"key":"359_CR159","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2019.103381","volume":"112","author":"S Byun","year":"2019","unstructured":"Byun, S., Kim, A., Jang, E., Kim, S., Choi, K., Yu, H., Jeon, H.: Detection of major depressive disorder from linear and nonlinear heart rate variability features during mental task protocol. Comput. Biol. Med. 112, 103381 (2019). https:\/\/doi.org\/10.1016\/j.compbiomed.2019.103381","journal-title":"Comput. Biol. Med."},{"key":"359_CR160","doi-asserted-by":"crossref","unstructured":"Alghowinem, S., Goecke, R., Wagner, M., Parker, G., Breakspear, M.: Eye movement analysis for depression detection. 2013 IEEE International Conference on Image Processing, ICIP 2013 - Proceedings. pp. 4220\u20134224 (2013)","DOI":"10.1109\/ICIP.2013.6738869"},{"key":"359_CR161","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2022.106132","volume":"150","author":"A Akbulut","year":"2022","unstructured":"Akbulut, A., Gungor, F., Tarakci, E., Aydin, M., Zaim, A., Catal, C.: Identification of phantom movements with an ensemble learning approach. Comput. Biol. Med. 150, 106132 (2022). https:\/\/doi.org\/10.1016\/j.compbiomed.2022.106132","journal-title":"Comput. Biol. Med."},{"key":"359_CR162","doi-asserted-by":"crossref","unstructured":"Yang, L., Pei, E., Sahli, H., Oveneke, M., Xia, X., Jiang, D.: Hybrid depression classification and estimation from audio video and text information. AVEC 2017 - Proceedings of the 7th Annual Workshop on Audio\/Visual Emotion Challenge, Co-located With MM 2017. pp. 45\u201346 (2017)","DOI":"10.1145\/3133944.3133950"},{"key":"359_CR163","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3390\/s20123599","volume":"20","author":"B Sumali","year":"2020","unstructured":"Sumali, B., Mitsukura, Y., Liang, K., Yoshimura, M., Kitazawa, M., Takamiya, A., Fujita, T., Mimura, M., Kishimoto, T.: Speech quality feature analysis for classification of depression and dementia patients. Sensors (Switzerland). 20, 1\u201317 (2020)","journal-title":"Sensors (Switzerland)."},{"key":"359_CR164","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12938-022-01036-0","volume":"21","author":"A Gaber","year":"2022","unstructured":"Gaber, A., Taher, M., Wahed, M., Shalaby, N., Gaber, S.: Classification of facial paralysis based on machine learning techniques. Biomed. Eng. Online 21, 1\u201320 (2022). https:\/\/doi.org\/10.1186\/s12938-022-01036-0","journal-title":"Biomed. Eng. Online"},{"key":"359_CR165","doi-asserted-by":"publisher","first-page":"46","DOI":"10.1016\/j.specom.2022.07.006","volume":"143","author":"X Miao","year":"2022","unstructured":"Miao, X., Li, Y., Wen, M., Liu, Y., Julian, I., Guo, H.: Fusing features of speech for depression classification based on higher-order spectral analysis. Speech Commun. 143, 46\u201356 (2022). https:\/\/doi.org\/10.1016\/j.specom.2022.07.006","journal-title":"Speech Commun."},{"key":"359_CR166","doi-asserted-by":"publisher","first-page":"2265","DOI":"10.1109\/JBHI.2019.2938247","volume":"23","author":"X Zhang","year":"2019","unstructured":"Zhang, X., Shen, J., Din, Z., Liu, J., Wang, G., Hu, B.: Multimodal depression detection: fusion of electroencephalography and paralinguistic behaviors using a novel strategy for classifier ensemble. IEEE J. Biomed. Health Inform. 23, 2265\u20132275 (2019)","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"359_CR167","doi-asserted-by":"crossref","unstructured":"Yang, L., Jiang, D., He, L., Pei, E., Oveneke, M., Sahli, H.: Decision tree based depression classification from audio video and language information. AVEC 2016 - Proceedings of the 6th International Workshop on Audio\/Visual Emotion Challenge, Co-located with ACM Multimedia 2016. pp. 89\u201396 (2016)","DOI":"10.1145\/2988257.2988269"},{"key":"359_CR168","doi-asserted-by":"publisher","DOI":"10.1007\/s10462-023-10493-5","author":"M Bhushan","year":"2023","unstructured":"Bhushan, M., Pandit, A., Garg, A.: Machine learning and deep learning techniques for the analysis of heart disease: a systematic literature review, open challenges and future directions. Artif. Intell. Rev. (2023). https:\/\/doi.org\/10.1007\/s10462-023-10493-5","journal-title":"Artif. Intell. Rev."},{"key":"359_CR169","doi-asserted-by":"publisher","DOI":"10.1016\/j.jksuci.2020.04.004","author":"A Khan","year":"2020","unstructured":"Khan, A., Zubair, S.: An improved multi-modal based machine learning approach for the prognosis of Alzheimer\u2019s disease. J. King Saud Univ.- Comput. Inf. Sci. (2020). https:\/\/doi.org\/10.1016\/j.jksuci.2020.04.004","journal-title":"J. King Saud Univ.- Comput. Inf. Sci."},{"key":"359_CR170","doi-asserted-by":"publisher","first-page":"87","DOI":"10.18280\/ts.390109","volume":"39","author":"N Janardhan","year":"2022","unstructured":"Janardhan, N., Kumaresh, N.: Improving depression prediction accuracy using fisher score-based feature selection and dynamic ensemble selection approach based on acoustic features of speech. Traitement Du Signal. 39, 87\u2013107 (2022)","journal-title":"Traitement Du Signal."},{"key":"359_CR171","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2021.104450","volume":"134","author":"S Sharma","year":"2021","unstructured":"Sharma, S., Singh, G., Sharma, M.: A comprehensive review and analysis of supervised-learning and soft computing techniques for stress diagnosis in humans. Comput. Biol. Med. 134, 104450 (2021). https:\/\/doi.org\/10.1016\/j.compbiomed.2021.104450","journal-title":"Comput. Biol. Med."},{"key":"359_CR172","first-page":"1","volume":"11","author":"N Iyortsuun","year":"2023","unstructured":"Iyortsuun, N., Kim, S., Jhon, M., Yang, H., Pant, S.: A review of machine learning and deep learning approaches on mental health diagnosis. Healthcare (Switzerland). 11, 1\u201327 (2023)","journal-title":"Healthcare (Switzerland)."},{"key":"359_CR173","doi-asserted-by":"crossref","unstructured":"Alghowinem, S., Goecke, R., Cohn, J., Wagner, M., Parker, G., Breakspear, M.: Cross-cultural detection of depression from nonverbal behaviour. 2015 11th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition, FG 2015. 1, 1\u20138 (2015)","DOI":"10.1109\/FG.2015.7163113"},{"key":"359_CR174","unstructured":"Alghowinem, S., Goecke, R., Wagner, M., Epps, J., Breakspear, M., Parker, G.: From joyous to clinically depressed: mood detection using spontaneous speech. Proceedings of the 25th International Florida Artificial Intelligence Research Society Conference, FLAIRS-25, 141\u2013146 (2012)"},{"key":"359_CR175","doi-asserted-by":"publisher","first-page":"142","DOI":"10.1109\/T-AFFC.2012.38","volume":"4","author":"Y Yang","year":"2013","unstructured":"Yang, Y., Fairbairn, C., Cohn, J.: Detecting depression severity from vocal prosody. IEEE Trans. Affect. Comput. 4, 142\u2013150 (2013)","journal-title":"IEEE Trans. Affect. Comput."},{"key":"359_CR176","doi-asserted-by":"crossref","unstructured":"Valstar, M., Schuller, B., Smith, K., Eyben, F., Jiang, B., Bilakhia, S., Schnieder, S., Cowie, R., Pantic, M.: AVEC 2013 - The continuous Audio\/Visual Emotion and depression recognition challenge. AVEC 2013 - Proceedings of the 3rd ACM International Workshop on Audio\/Visual Emotion Challenge. pp. 3\u201310 (2013)","DOI":"10.1145\/2512530.2512533"},{"key":"359_CR177","doi-asserted-by":"crossref","unstructured":"Stratou, G., Scherer, S., Gratch, J., Morency, L.: Automatic nonverbal behavior indicators of depression and PTSD: Exploring gender differences. Proceedings - 2013 Humaine Association Conference on Affective Computing and Intelligent Interaction, ACII 2013. pp. 147\u2013152 (2013)","DOI":"10.1109\/ACII.2013.31"},{"key":"359_CR178","doi-asserted-by":"publisher","DOI":"10.3389\/fcomp.2023.990426","volume":"5","author":"S Alghowinem","year":"2023","unstructured":"Alghowinem, S., Zhang, X., Breazeal, C., Park, H.: Multimodal region-based behavioral modeling for suicide risk screening. Front. Comput. Sci. 5, 990426 (2023)","journal-title":"Front. Comput. Sci."},{"key":"359_CR179","doi-asserted-by":"publisher","first-page":"27","DOI":"10.1016\/j.specom.2015.09.003","volume":"75","author":"N Cummins","year":"2015","unstructured":"Cummins, N., Sethu, V., Epps, J., Schnieder, S., Krajewski, J.: Analysis of acoustic space variability in speech affected by depression. Speech Commun. 75, 27\u201349 (2015). https:\/\/doi.org\/10.1016\/j.specom.2015.09.003","journal-title":"Speech Commun."},{"key":"359_CR180","first-page":"9","volume":"2017\u2013Augus","author":"Z Shah","year":"2017","unstructured":"Shah, Z., Sidorov, K., Marshall, D.: Psychomotor cues for depression screening. Int. Conf. Digit. Signal Proces. DSP. 2017\u2013Augus, 9\u201313 (2017)","journal-title":"Int. Conf. Digit. Signal Proces. DSP."},{"key":"359_CR181","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1016\/S0167-7012(00)00201-3","volume":"43","author":"I Basheer","year":"2000","unstructured":"Basheer, I., Hajmeer, M.: Artificial neural networks: fundamentals, computing, design, and application. J. Microbiol. Methods 43, 3\u201331 (2000)","journal-title":"J. Microbiol. Methods"},{"key":"359_CR182","doi-asserted-by":"crossref","unstructured":"Alghowinem Multimodal region-based behavioral modeling for suicide risk screening. Frontiers In Computer Science. 5 (2023)","DOI":"10.3389\/fcomp.2023.990426"},{"key":"359_CR183","doi-asserted-by":"crossref","unstructured":"Ghosh, S., Chatterjee, M., Morency, L.: A multimodal context-based approach for distress assessment. Proceedings of the 16th International Conference on Multimodal Interaction. pp. 240\u2013246 (2014)","DOI":"10.1145\/2663204.2663274"},{"key":"359_CR184","first-page":"1793","volume":"3","author":"T Jayalakshmi","year":"2011","unstructured":"Jayalakshmi, T., Santhakumaran, A.: Statistical normalization and back propagation for classification. Int. J. Comput. Theory Eng. 3, 1793\u20138201 (2011)","journal-title":"Int. J. Comput. Theory Eng."},{"key":"359_CR185","doi-asserted-by":"crossref","unstructured":"Vedula, N., Parthasarathy, S.: Emotional and linguistic cues of depression from social media. Proceedings of the 2017 International Conference on Digital Health. pp. 127\u2013136 (2017)","DOI":"10.1145\/3079452.3079465"},{"key":"359_CR186","doi-asserted-by":"crossref","unstructured":"Thati, R., Mamidisetti, S.: Apparent personality traits detection based on correlation-based attention and feature weighting methods. Advances In Computational Intelligence And Informatics. pp. 399-405 (2024)","DOI":"10.1007\/978-981-97-4727-6_40"},{"key":"359_CR187","doi-asserted-by":"publisher","DOI":"10.1016\/j.jbi.2019.103371","volume":"103","author":"M Masud","year":"2020","unstructured":"Masud, M., Mamun, M., Thapa, K., Lee, D., Griffiths, M., Yang, S.: Unobtrusive monitoring of behavior and movement patterns to detect clinical depression severity level via smartphone. J. Biomed. Inform. 103, 103371 (2020). https:\/\/doi.org\/10.1016\/j.jbi.2019.103371","journal-title":"J. Biomed. Inform."},{"key":"359_CR188","doi-asserted-by":"publisher","first-page":"43","DOI":"10.1016\/j.inffus.2019.06.008","volume":"53","author":"D Kelly","year":"2020","unstructured":"Kelly, D., Condell, J., Curran, K., Caulfield, B.: A multimodal smartphone sensor system for behaviour measurement and health status inference. Inf. Fusion. 53, 43\u201354 (2020). https:\/\/doi.org\/10.1016\/j.inffus.2019.06.008","journal-title":"Inf. Fusion."},{"key":"359_CR189","doi-asserted-by":"crossref","unstructured":"Sano A.: Identifying objective physiological markers and modifiable behaviors for self-reported stress and mental health status using wearable sensors and mobile phones: observational study. (2018)","DOI":"10.2196\/preprints.9410"},{"key":"359_CR190","unstructured":"Lozano, A.: Tell me your apps and i will tell you your mood: Correlation of apps usage with Bipolar Disorder State. ACM International Conference Proceeding Series. 2014 (2014)"},{"key":"359_CR191","doi-asserted-by":"crossref","unstructured":"Kraepelin, E.: Manic-depressive insanity and paranoia. (1921)","DOI":"10.1097\/00005053-192104000-00057"},{"key":"359_CR192","unstructured":"Beck, Depression: clinical, experimental, and theoretical aspects. (1967)"},{"key":"359_CR193","unstructured":"Kitchenham, B.: Procedures for performing systematic reviews. Keele, UK, Keele University. 33, 1\u201326 (2004)"},{"key":"359_CR194","unstructured":"ACM, ACM Digital Library, [Online]. Available: http:\/\/dl.acm.org. Accessed July 2024"},{"key":"359_CR195","unstructured":"IEEE, IEEE Xplore Digital Library, [Online]. Available: https:\/\/ieeexplore.ieee.org\/Xplore\/home.jsp. Accessed July 2024"},{"key":"359_CR196","unstructured":"Elsevier, ScienceDirect, [Online]. Available: https:\/\/journalfinder.elsevier.com\/. Accessed July 2024"},{"key":"359_CR197","doi-asserted-by":"publisher","first-page":"2340008","DOI":"10.1142\/S0218213023400080","volume":"32","author":"R Thati","year":"2023","unstructured":"Thati, R., Dhadwal, A., Kumar, P., Sainaba, P.: Multimodal Depression Detection: Using Fusion Strategies with Smart Phone Usage and Audio-visual Behavior. Int. J. Artif. Intell. Tools 32, 2340008 (2023)","journal-title":"Int. J. Artif. Intell. Tools"},{"key":"359_CR198","unstructured":"Springer, SpringerLink, [Online]. Available: http:\/\/link.springer.com. Accessed July 2024"},{"key":"359_CR199","unstructured":"Wiley, Wiley Online Library, [Online]. Available: https:\/\/onlinelibrary.wiley.com\/. Accessed July 2024"},{"key":"359_CR200","unstructured":"Scopus, Scopus, [Online]. Available: http:\/\/www.scopus.com. Accessed in July 2024"},{"key":"359_CR201","unstructured":"Google Scholar, Google Scholar, [Online]. Available: https:\/\/scholar.google.com\/. Accessed July (2024)"},{"key":"359_CR202","doi-asserted-by":"publisher","first-page":"2340004","DOI":"10.1142\/S0218213023400043","volume":"32","author":"A Verma","year":"2023","unstructured":"Verma, A., Jain, P., Kumar, T.: An effective depression diagnostic system using speech signal analysis through deep learning methods. Int. J. Artif. Intell. Tools 32, 2340004 (2023)","journal-title":"Int. J. Artif. Intell. Tools"},{"key":"359_CR203","first-page":"609","volume":"182","author":"R Flores","year":"2022","unstructured":"Flores, R., Tlachac, M., Toto, E., Rundensteiner, E.: AudiFace: multimodal deep learning for depression screening. Proc. Mach. Learn. Res. 182, 609\u2013630 (2022)","journal-title":"Proc. Mach. Learn. Res."},{"key":"359_CR204","doi-asserted-by":"publisher","first-page":"234","DOI":"10.1007\/s43657-023-00152-8","volume":"4","author":"W Zhang","year":"2024","unstructured":"Zhang, W., Mao, K., Chen, J.: A multimodal approach for detection and assessment of depression using text. Audio Video. Phenom. 4, 234\u2013249 (2024). https:\/\/doi.org\/10.1007\/s43657-023-00152-8","journal-title":"Audio Video. Phenom."},{"key":"359_CR205","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1111\/exsy.12933","volume":"40","author":"P Meshram","year":"2023","unstructured":"Meshram, P., Rambola, R.: Diagnosis of depression level using multimodal approaches using deep learning techniques with multiple selective features. Expert. Syst. 40, 1\u201313 (2023)","journal-title":"Expert. Syst."},{"key":"359_CR206","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2022.119007","volume":"213","author":"T Ghosh","year":"2023","unstructured":"Ghosh, T., Banna, M., Nahian, M., Uddin, M., Kaiser, M., Mahmud, M.: An attention-based hybrid architecture with explainability for depressive social media text detection in Bangla. Expert Syst. Appl. 213, 119007 (2023). https:\/\/doi.org\/10.1016\/j.eswa.2022.119007","journal-title":"Expert Syst. Appl."},{"key":"359_CR207","doi-asserted-by":"publisher","DOI":"10.2196\/45991","volume":"7","author":"J Kim","year":"2023","unstructured":"Kim, J., Wang, B., Kim, M., Lee, J., Kim, H., Roh, D., Lee, K., Hong, S., Lim, J., Kim, J., Ryan, N.: Prediction of diagnosis and treatment response in adolescents with depression by using a smartphone app and deep learning approaches: usability Study. JMIR Form Res. 7, e45991 (2023). (http:\/\/www.ncbi.nlm.nih.gov\/pubmed\/37223978)","journal-title":"JMIR Form Res."},{"key":"359_CR208","doi-asserted-by":"crossref","unstructured":"Sumali, B., Mitsukura, Y., Tazawa, Y., Kishimoto, T.: facial landmark activity features for depression screening. 2019 58th Annual Conference of the Society Of Instrument and Control Engineers of Japan (SICE). pp. 1376\u20131381 (2019)","DOI":"10.23919\/SICE.2019.8859798"},{"key":"359_CR209","doi-asserted-by":"publisher","first-page":"578","DOI":"10.1109\/TAFFC.2017.2650899","volume":"9","author":"Y Zhu","year":"2017","unstructured":"Zhu, Y., Shang, Y., Shao, Z., Guo, G.: Automated depression diagnosis based on deep networks to encode facial appearance and dynamics. IEEE Trans. Affect. Comput. 9, 578\u2013584 (2017)","journal-title":"IEEE Trans. Affect. Comput."},{"key":"359_CR210","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2021.104499","volume":"135","author":"R Chiong","year":"2021","unstructured":"Chiong, R., Budhi, G., Dhakal, S., Chiong, F.: A textual-based featuring approach for depression detection using machine learning classifiers and social media texts. Comput. Biol. Med. 135, 104499 (2021)","journal-title":"Comput. Biol. Med."},{"key":"359_CR211","doi-asserted-by":"publisher","first-page":"43","DOI":"10.1007\/s13042-017-0697-1","volume":"10","author":"Z Peng","year":"2019","unstructured":"Peng, Z., Hu, Q., Dang, J.: Multi-kernel SVM based depression recognition using social media data. Int. J. Mach. Learn. Cybern. 10, 43\u201357 (2019)","journal-title":"Int. J. Mach. Learn. Cybern."},{"key":"359_CR212","unstructured":"Silva, W., Lopes, L., Galdino, M., Almeida, A.: Voice acoustic parameters as predictors of depression. Journal Of Voice. (2021)"},{"key":"359_CR213","doi-asserted-by":"publisher","first-page":"72","DOI":"10.1159\/000500354","volume":"3","author":"Y Ozkanca","year":"2019","unstructured":"Ozkanca, Y., Ekmekci, M., Atkins, D., Demiroglu, C., Hosseini Ghomi, R.: Depression screening from voice samples of patients affected by parkinson\u2019s disease. Digital Biomark. 3, 72\u201382 (2019)","journal-title":"Digital Biomark."},{"key":"359_CR214","doi-asserted-by":"crossref","unstructured":"Bobade, P., Vani, M.: Stress detection with machine learning and deep learning using multimodal physiological data. Proceedings of the 2nd International Conference on Inventive Research in Computing Applications, ICIRCA 2020. pp. 51\u201357 (2020)","DOI":"10.1109\/ICIRCA48905.2020.9183244"},{"key":"359_CR215","doi-asserted-by":"crossref","unstructured":"Sano, A., Picard, R.: Stress recognition using wearable sensors and mobile phones. Proceedings - 2013 Humaine Association Conference On Affective Computing And Intelligent Interaction, ACII 2013. pp. 671\u2013676 (2013)","DOI":"10.1109\/ACII.2013.117"},{"key":"359_CR216","doi-asserted-by":"crossref","unstructured":"Dias, L., Barbosa, J., Feij\u00f3, L., Vianna, H.: Development and testing of iAware model for ubiquitous care of patients with symptoms of stress, anxiety and depression. Computer Methods And Programs Biomedicine. 187 (2020)","DOI":"10.1016\/j.cmpb.2019.105113"},{"key":"359_CR217","doi-asserted-by":"crossref","unstructured":"Zhang, L., Driscol, J., Chen, X., Ghomi, R.: Evaluating acoustic and linguistic features of detecting depression sub-challenge dataset. AVEC 2019 - Proceedings of the 9th International Audio\/Visual Emotion Challenge and Workshop, Co-located with MM 2019, 47\u201353 (2019)","DOI":"10.1145\/3347320.3357693"},{"key":"359_CR218","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.specom.2019.10.003","volume":"115","author":"B Stasak","year":"2019","unstructured":"Stasak, B., Epps, J., Goecke, R.: Automatic depression classification based on affective read sentences: opportunities for text-dependent analysis. Speech Commun. 115, 1\u201314 (2019)","journal-title":"Speech Commun."},{"key":"359_CR219","doi-asserted-by":"publisher","first-page":"1320","DOI":"10.1109\/JAS.2025.125393","volume":"12","author":"A Survey","year":"2025","unstructured":"Survey, A., Li, Q., Liu, X., Hu, X., Rahman Ahad, M., Ren, M., Yao, L., Huang, Y.: Machine learning-based prediction of depressive disorders via various data modalities. IEEE\/CAA J. Automatica Sinica. 12, 1320\u20131349 (2025)","journal-title":"IEEE\/CAA J. Automatica Sinica."},{"key":"359_CR220","doi-asserted-by":"crossref","unstructured":"Pampouchidou, A., Simantiraki, O., Vazakopoulou, C., Chatzaki, C., Pediaditis, M., Maridaki, A., Marias, K., Simos, P., Yang, F., Meriaudeau, F., Tsiknakis, M.: Facial geometry and speech analysis for depression detection. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS. pp. 1433\u20131436 (2017)","DOI":"10.1109\/EMBC.2017.8037103"},{"key":"359_CR221","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41746-025-01933-3","volume":"8","author":"L Wang","year":"2025","unstructured":"Wang, L., Wang, C., Li, C., Murai, T., Bai, Y., Song, Z., Zhang, S., Zhang, Q., Huang, Y., Bi, X., Jiang, J.: AI-assisted multi-modal information for the screening of depression: a systematic review and meta-analysis. Npj Digit. Med. 8, 1\u201314 (2025)","journal-title":"Npj Digit. Med."},{"key":"359_CR222","doi-asserted-by":"publisher","first-page":"478","DOI":"10.1109\/TAFFC.2016.2634527","volume":"9","author":"S Alghowinem","year":"2018","unstructured":"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. IEEE Trans. Affect. Comput. 9, 478\u2013490 (2018)","journal-title":"IEEE Trans. Affect. Comput."},{"key":"359_CR223","doi-asserted-by":"publisher","DOI":"10.1007\/s00406-025-01990-5","author":"J Eder","year":"2025","unstructured":"Eder, J., Dong, M., W\u00f6hler, M., Simon, M., Glocker, C., Pfeiffer, L., Gaus, R., Wolf, J., Mestan, K., Krcmar, H., Koutsouleris, N., Schneider, A., Gensichen, J., Musil, R., Falkai, P.: Group, F: a multimodal approach to depression diagnosis: insights from machine learning algorithm development in primary care. Eur. Arch. Psychiatry Clin. Neurosci. (2025). https:\/\/doi.org\/10.1007\/s00406-025-01990-5","journal-title":"Eur. Arch. Psychiatry Clin. Neurosci."},{"key":"359_CR224","doi-asserted-by":"crossref","unstructured":"Pan, Z., Ma, H., Zhang, L., Wang, Y.: Depression detection based on reaction time and eye movement Zeyu Pan Huimin Ma Lin Zhang Yahui Wang Tsinghua National Laboratory for Information Science and Technology ( TNList ) Department of Electronic Engineering , Tsinghua University, Beijing 100084. 2019 IEEE International Conference On Image Processing (ICIP). pp. 2184-2188 (2019)","DOI":"10.1109\/ICIP.2019.8803181"},{"key":"359_CR225","doi-asserted-by":"crossref","unstructured":"Ray, A., Kumar, S., Reddy, R., Mukherjee, P., Garg, R.: Multi-level attention network using text, audio and video for depression prediction. AVEC 2019 - Proceedings of the 9th International Audio\/Visual Emotion Challenge and Workshop, Co-located With MM 2019. pp. 81\u201388 (2019)","DOI":"10.1145\/3347320.3357697"},{"key":"359_CR226","doi-asserted-by":"crossref","unstructured":"Nasir, M., Jati, A., Shivakumar, P., Chakravarthula, S., Georgiou, P.: Multimodal and multiresolution depression detection from speech and facial landmark features. AVEC 2016 - Proceedings of the 6th International Workshop on Audio\/Visual Emotion Challenge, Co-located with ACM Multimedia 2016. pp. 43\u201350 (2016)","DOI":"10.1145\/2988257.2988261"},{"key":"359_CR227","doi-asserted-by":"publisher","first-page":"493","DOI":"10.1007\/s10044-021-01001-y","volume":"25","author":"F Ceccarelli","year":"2022","unstructured":"Ceccarelli, F., Mahmoud, M.: Multimodal temporal machine learning for bipolar disorder and depression recognition. Pattern Anal. Appl. 25, 493\u2013504 (2022). https:\/\/doi.org\/10.1007\/s10044-021-01001-y","journal-title":"Pattern Anal. Appl."},{"key":"359_CR228","doi-asserted-by":"crossref","unstructured":"Govind, Ansari, G., Sharma, A., Arya, P., Saxena, Y.: Multimodal Depression Detection System Using Machine Learning. Proceedings of 2023 2nd International Conference on Informatics, ICI 2023. pp. 1\u20137 (2023)","DOI":"10.1109\/ICI60088.2023.10421362"},{"key":"359_CR229","doi-asserted-by":"crossref","unstructured":"Ciftci, E., Kaya, H., Gulec, H., Salah, A.: The Turkish Audio-Visual Bipolar Disorder Corpus. 2018 1st Asian Conference on Affective Computing and Intelligent Interaction, ACII Asia 2018. (2018)","DOI":"10.1109\/ACIIAsia.2018.8470362"},{"key":"359_CR230","doi-asserted-by":"publisher","first-page":"79","DOI":"10.1016\/j.cosrev.2017.07.002","volume":"25","author":"E Politou","year":"2017","unstructured":"Politou, E., Alepis, E., Patsakis, C.: A survey on mobile affective computing. Comput. Sci. Rev. 25, 79\u2013100 (2017). https:\/\/doi.org\/10.1016\/j.cosrev.2017.07.002","journal-title":"Comput. Sci. Rev."}],"container-title":["Iran Journal of Computer Science"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s42044-025-00359-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s42044-025-00359-0","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s42044-025-00359-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,4]],"date-time":"2026-01-04T11:02:19Z","timestamp":1767524539000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s42044-025-00359-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,1,4]]},"references-count":230,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2026,12]]}},"alternative-id":["359"],"URL":"https:\/\/doi.org\/10.1007\/s42044-025-00359-0","relation":{},"ISSN":["2520-8438","2520-8446"],"issn-type":[{"type":"print","value":"2520-8438"},{"type":"electronic","value":"2520-8446"}],"subject":[],"published":{"date-parts":[[2026,1,4]]},"assertion":[{"value":"2 May 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 December 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 January 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"15"}}