{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,20]],"date-time":"2026-05-20T13:10:37Z","timestamp":1779282637730,"version":"3.51.4"},"reference-count":29,"publisher":"Springer Science and Business Media LLC","issue":"6","license":[{"start":{"date-parts":[[2026,5,20]],"date-time":"2026-05-20T00:00:00Z","timestamp":1779235200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,5,20]],"date-time":"2026-05-20T00:00:00Z","timestamp":1779235200000},"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":["Multimed Tools Appl"],"DOI":"10.1007\/s11042-026-21691-y","type":"journal-article","created":{"date-parts":[[2026,5,20]],"date-time":"2026-05-20T12:33:47Z","timestamp":1779280427000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Explainable AI for multimodal stress detection: interpreting model decisions across physiological, video and audio modalities"],"prefix":"10.1007","volume":"85","author":[{"given":"Andrea Francesco","family":"Abate","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Carmen","family":"Bisogni","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Aniello","family":"Castiglione","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-9377-2925","authenticated-orcid":false,"given":"Maddalena","family":"Migliaccio","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,5,20]]},"reference":[{"key":"21691_CR1","doi-asserted-by":"publisher","unstructured":"Paniagua-G\u00f3mez M, Fernandez-Carmona M (2025) Trends and challenges in real-time stress detection and modulation: The role of the IoT and artificial intelligence. Electronics 14(13). https:\/\/doi.org\/10.3390\/electronics14132581","DOI":"10.3390\/electronics14132581"},{"issue":"2","key":"21691_CR2","doi-asserted-by":"publisher","first-page":"227","DOI":"10.1109\/TAFFC.2016.2610975","volume":"9","author":"S Koldijk","year":"2018","unstructured":"Koldijk S, Neerincx MA, Kraaij W (2018) Detecting Work Stress in Offices by Combining Unobtrusive Sensors. IEEE Trans Affect Comput 9(2):227\u2013239. https:\/\/doi.org\/10.1109\/TAFFC.2016.2610975","journal-title":"IEEE Trans Affect Comput"},{"key":"21691_CR3","doi-asserted-by":"publisher","unstructured":"K MN, D M, M K (2024) Stress monitoring with computer vision and machine learning for software employees. In: 2024 2nd International Conference on Sustainable Computing and Smart Systems (ICSCSS), pp 1016\u20131021. https:\/\/doi.org\/10.1109\/ICSCSS60660.2024.10625637","DOI":"10.1109\/ICSCSS60660.2024.10625637"},{"key":"21691_CR4","doi-asserted-by":"publisher","unstructured":"Ming FJ, Shabana\u00a0Anhum S, Islam S, Keoy KH (2023) Facial Emotion Recognition System for Mental Stress Detection among University Students. In: 2023 3rd International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME), pp 1\u20136. https:\/\/doi.org\/10.1109\/ICECCME57830.2023.10252617","DOI":"10.1109\/ICECCME57830.2023.10252617"},{"key":"21691_CR5","doi-asserted-by":"publisher","unstructured":"Shinde S, Ghotkar A (2025) Mental stress detection with the multimodal data using ensemble optimization enabled explainable convolutional neural network. Biomed Mater Dev 4. https:\/\/doi.org\/10.1007\/s44174-025-00296-3","DOI":"10.1007\/s44174-025-00296-3"},{"key":"21691_CR6","doi-asserted-by":"publisher","unstructured":"Choi H-S (2025) Emotion recognition using a siamese model and a late fusion-based multimodal method in the WESAD dataset with hardware accelerators. Electronics 14(4). https:\/\/doi.org\/10.3390\/electronics14040723","DOI":"10.3390\/electronics14040723"},{"issue":"2","key":"21691_CR7","doi-asserted-by":"publisher","first-page":"156","DOI":"10.1109\/TITS.2005.848368","volume":"6","author":"JA Healey","year":"2005","unstructured":"Healey JA, Picard RW (2005) Detecting Stress During Real-World Driving Tasks Using Physiological Sensors. IEEE Trans Intell Transp Syst 6(2):156\u2013166. https:\/\/doi.org\/10.1109\/TITS.2005.848368","journal-title":"IEEE Trans Intell Transp Syst"},{"key":"21691_CR8","doi-asserted-by":"publisher","unstructured":"Schmidt P, Reiss A, Duerichen R, Marberger C, Van\u00a0Laerhoven K (2018) Introducing WESAD, a multimodal dataset for Wearable Stress and Affect Detection. In: Proceedings of the 20th ACM international conference on multimodal interaction. ICMI \u201918, pp 400\u2013408. Association for Computing Machinery, New York, NY, USA. https:\/\/doi.org\/10.1145\/3242969.3242985","DOI":"10.1145\/3242969.3242985"},{"key":"21691_CR9","doi-asserted-by":"publisher","unstructured":"Koldijk S, Sappelli M, Verberne S, Neerincx MA, Kraaij W (2014) The SWELL knowledge work dataset for stress and user modeling research. In: Proceedings of the 16th International Conference on Multimodal Interaction (ICMI), pp 291\u2013298. https:\/\/doi.org\/10.1145\/2663204.2663257","DOI":"10.1145\/2663204.2663257"},{"issue":"3","key":"21691_CR10","doi-asserted-by":"publisher","first-page":"494","DOI":"10.3390\/signals5030026","volume":"5","author":"MM Abdel-Latif","year":"2024","unstructured":"Abdel-Latif MM, Rashid MM, Askari MR, Shahidehpour A, Ahmadasas M, Park M, Sharp L, Quinn L, Cinar A (2024) Acute psychological stress detection using explainable artificial intelligence for automated insulin delivery. Signals 5(3):494\u2013507. https:\/\/doi.org\/10.3390\/signals5030026","journal-title":"Signals"},{"key":"21691_CR11","doi-asserted-by":"publisher","unstructured":"Moser MK, Ehrhart M, Resch B (2024) An Explainable Deep Learning Approach for Stress Detection in Wearable Sensor Measurements. Sensors 24(16). https:\/\/doi.org\/10.3390\/s24165085","DOI":"10.3390\/s24165085"},{"key":"21691_CR12","doi-asserted-by":"publisher","first-page":"107960","DOI":"10.1016\/j.bspc.2025.107960","volume":"109","author":"H Lee","year":"2025","unstructured":"Lee H, Kim J, Han B, Park SM, Chang J (2025) Developing an explainable Deep Neural Network for stress detection using biosignals and human-engineered features. Biomed Signal Process Control 109:107960. https:\/\/doi.org\/10.1016\/j.bspc.2025.107960","journal-title":"Biomed Signal Process Control"},{"key":"21691_CR13","doi-asserted-by":"publisher","unstructured":"Swedheetha C, Sankar EN, Ramkishan CB (2025) Mental stress prediction using machine learning and facial emotion recognition. In: 2025 IEEE 14th International Conference on Communication Systems and Network Technologies (CSNT), pp 239\u2013244. https:\/\/doi.org\/10.1109\/CSNT64827.2025.10968217","DOI":"10.1109\/CSNT64827.2025.10968217"},{"key":"21691_CR14","doi-asserted-by":"publisher","unstructured":"Upadhya J, Poudel K, Ranganathan J (2024) A comprehensive approach to early detection of workplace stress with multi-modal analysis and explainable AI. In: Proceedings of the 2024 computers and people research conference. SIGMIS-CPR \u201924. Association for Computing Machinery, New York, NY, USA. https:\/\/doi.org\/10.1145\/3632634.3655878","DOI":"10.1145\/3632634.3655878"},{"key":"21691_CR15","doi-asserted-by":"publisher","first-page":"108507","DOI":"10.1016\/j.cmpb.2024.108507","volume":"259","author":"G Giannakakis","year":"2025","unstructured":"Giannakakis G, Roussos A, Andreou C, Borgwardt S, Korda AI (2025) Stress recognition identifying relevant facial action units through explainable artificial intelligence and machine learning. Comput Methods Programs Biomed 259:108507. https:\/\/doi.org\/10.1016\/j.cmpb.2024.108507","journal-title":"Comput Methods Programs Biomed"},{"key":"21691_CR16","doi-asserted-by":"publisher","unstructured":"Chatterjee A, Riegler M, Ganesh K, Halvorsen P (2025) Stress management with HRV following AI, semantic ontology, genetic algorithm and tree explainer. Scientif Rep 15. https:\/\/doi.org\/10.1038\/s41598-025-87510-w","DOI":"10.1038\/s41598-025-87510-w"},{"key":"21691_CR17","unstructured":"Chaptoukaev H, Strizhkova V, Panariello M, D\u2019Alpaos B, Reka A, Manera V, Th\u00fcmmler S, Ismailova E, Evans N, Bremond F, Todisco M, Zuluaga MA, Ferrari LM (2023) StressID: a multimodal dataset for stress identification. In: Thirty-seventh conference on neural information processing systems datasets and benchmarks track. This work was carried out using StressID: a Multimodal Dataset for Stress Identification\u2014Inria, EURECOM. https:\/\/openreview.net\/forum?id=qWsQi9DGJb"},{"key":"21691_CR18","doi-asserted-by":"publisher","first-page":"4597","DOI":"10.1109\/ACCESS.2024.3525459","volume":"13","author":"E Abdelfattah","year":"2025","unstructured":"Abdelfattah E, Joshi S, Tiwari S (2025) Machine and deep learning models for stress detection using multimodal physiological data. IEEE Access 13:4597\u20134608. https:\/\/doi.org\/10.1109\/ACCESS.2024.3525459","journal-title":"IEEE Access"},{"key":"21691_CR19","doi-asserted-by":"publisher","first-page":"169310","DOI":"10.1109\/ACCESS.2024.3463742","volume":"12","author":"S Shikha","year":"2024","unstructured":"Shikha S, Sethia D, Indu S (2024) Optimization of wearable biosensor data for stress classification using machine learning and explainable ai. IEEE Access 12:169310\u2013169327. https:\/\/doi.org\/10.1109\/ACCESS.2024.3463742","journal-title":"IEEE Access"},{"key":"21691_CR20","doi-asserted-by":"publisher","unstructured":"Migliaccio M, Abate A, Bisogni C, Cimmino L (2025) Machine learning-driven stress prediction: A comparative analysis and web application using the sleep health and lifestyle dataset. In: 2025 6th International conference on Bio-engineering for Smart Technologies (BioSMART), pp 1\u20134. https:\/\/doi.org\/10.1109\/BioSMART66413.2025.11046172","DOI":"10.1109\/BioSMART66413.2025.11046172"},{"key":"21691_CR21","doi-asserted-by":"publisher","unstructured":"Narzary D, Sharma U, Khanna A (2024) An automated stress detection model based on dual approach of clinical psychologist prediction and machine learning. Intern J Inf Technol 17. https:\/\/doi.org\/10.1007\/s41870-024-02213-1","DOI":"10.1007\/s41870-024-02213-1"},{"key":"21691_CR22","doi-asserted-by":"publisher","unstructured":"Jaber D, Hajj H, Maalouf F, El-Hajj W (2022) Medically-oriented design for explainable ai for stress prediction from physiological measurements. BMC Med Inf Dec Mak 22. https:\/\/doi.org\/10.1186\/s12911-022-01772-2","DOI":"10.1186\/s12911-022-01772-2"},{"issue":"1","key":"21691_CR23","doi-asserted-by":"publisher","first-page":"18","DOI":"10.1109\/T-AFFC.2011.15","volume":"3","author":"S Koelstra","year":"2012","unstructured":"Koelstra S, Muhl C, Soleymani M, Lee J-S, Yazdani A, Ebrahimi T, Pun T, Nijholt A, Patras I (2012) DEAP: A Database for Emotion Analysis; Using Physiological Signals. IEEE Trans Affect Comput 3(1):18\u201331. https:\/\/doi.org\/10.1109\/T-AFFC.2011.15","journal-title":"IEEE Trans Affect Comput"},{"issue":"1","key":"21691_CR24","doi-asserted-by":"publisher","first-page":"42","DOI":"10.1109\/T-AFFC.2011.25","volume":"3","author":"M Soleymani","year":"2012","unstructured":"Soleymani M, Lichtenauer J, Pun T, Pantic M (2012) A Multimodal Database for Affect Recognition and Implicit Tagging. IEEE Trans Affect Comput 3(1):42\u201355. https:\/\/doi.org\/10.1109\/T-AFFC.2011.25","journal-title":"IEEE Trans Affect Comput"},{"key":"21691_CR25","unstructured":"Jaiswal M, Bara C-P, Luo Y, Burzo M, Mihalcea R, Provost EM (2020) MuSE: a multimodal dataset of stressed emotion. In: Calzolari N, B\u00e9chet F, Blache P, Choukri K, Cieri C, Declerck T, Goggi S, Isahara H, Maegaard B, Mariani J, Mazo H, Moreno A, Odijk J, Piperidis S (eds) Proceedings of the twelfth language resources and evaluation conference, pp 1499\u20131510. European Language Resources Association, Marseille, France. https:\/\/aclanthology.org\/2020.lrec-1.187\/"},{"issue":"23","key":"21691_CR26","doi-asserted-by":"publisher","first-page":"215","DOI":"10.1161\/01.CIR.101.23.e215","volume":"101","author":"AL Goldberger","year":"2000","unstructured":"Goldberger AL, Amaral LAN, Glass L, Hausdorff JM, Ivanov PC, Mark RG, Mietus JE, Moody GB, Peng C-K, Stanley HE (2000) Physiobank, physiotoolkit, and physionet. Circulation 101(23):215\u2013220. https:\/\/doi.org\/10.1161\/01.CIR.101.23.e215","journal-title":"Circulation"},{"issue":"1","key":"21691_CR27","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/sdata.2017.110","volume":"4","author":"S Taamneh","year":"2017","unstructured":"Taamneh S, Tsiamyrtzis P, Dcosta M, Buddharaju P, Khatri A, Manser M, Ferris T, Wunderlich R, Pavlidis I (2017) A multimodal dataset for various forms of distracted driving. Scientif Data 4(1):1\u201321. https:\/\/doi.org\/10.1038\/sdata.2017.110","journal-title":"Scientif Data"},{"key":"21691_CR28","doi-asserted-by":"publisher","unstructured":"Haouij NE, Poggi J-M, Sevestre-Ghalila S, Ghozi R, Ja\u00efdane M (2018) AffectiveROAD system and database to assess driver\u2019s attention. In: Proceedings of the 33rd annual ACM Symposium on Applied Computing. SAC \u201918, pp 800\u2013803. Association for Computing Machinery, New York, NY, USA. https:\/\/doi.org\/10.1145\/3167132.3167395","DOI":"10.1145\/3167132.3167395"},{"key":"21691_CR29","doi-asserted-by":"publisher","unstructured":"Prasada Rao K, Chandra Sekhara Rao MVP, Hemanth Chowdary N (2019) An integrated approach to emotion recognition and gender classification. J Vis Commun Image Represent 60:339\u2013345. https:\/\/doi.org\/10.1016\/j.jvcir.2019.03.002","DOI":"10.1016\/j.jvcir.2019.03.002"}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-026-21691-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11042-026-21691-y","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-026-21691-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,20]],"date-time":"2026-05-20T12:33:50Z","timestamp":1779280430000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11042-026-21691-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,5,20]]},"references-count":29,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2026,6]]}},"alternative-id":["21691"],"URL":"https:\/\/doi.org\/10.1007\/s11042-026-21691-y","relation":{},"ISSN":["1573-7721"],"issn-type":[{"value":"1573-7721","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,5,20]]},"assertion":[{"value":"4 November 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 April 2026","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 May 2026","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 May 2026","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Authors Andrea Francesco Abate, Carmen Bisogni, Aniello Castiglione and Maddalena Migliaccio are from the same institution as Guest Editors Lucia Cimmino and Matteo Polsinelli. The Guest Editors were not involved in the decision-making process related to this manuscript. Final decision was made by Harry Agius, Deputy Editor-in-Chief.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing Interests"}}],"article-number":"531"}}