{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,5]],"date-time":"2026-01-05T15:06:53Z","timestamp":1767625613548,"version":"3.40.3"},"publisher-location":"Cham","reference-count":28,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031246661"},{"type":"electronic","value":"9783031246678"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-031-24667-8_22","type":"book-chapter","created":{"date-parts":[[2023,1,31]],"date-time":"2023-01-31T09:03:44Z","timestamp":1675155824000},"page":"241-252","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Affective Human-Robot Interaction with\u00a0Multimodal Explanations"],"prefix":"10.1007","author":[{"given":"Hongbo","family":"Zhu","sequence":"first","affiliation":[]},{"given":"Chuang","family":"Yu","sequence":"additional","affiliation":[]},{"given":"Angelo","family":"Cangelosi","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,2,1]]},"reference":[{"key":"22_CR1","doi-asserted-by":"publisher","first-page":"52138","DOI":"10.1109\/ACCESS.2018.2870052","volume":"6","author":"A Adadi","year":"2018","unstructured":"Adadi, A., Berrada, M.: Peeking inside the black-box: a survey on explainable artificial intelligence (xai). IEEE Access 6, 52138\u201352160 (2018)","journal-title":"IEEE Access"},{"key":"22_CR2","doi-asserted-by":"publisher","first-page":"82","DOI":"10.1016\/j.inffus.2019.12.012","volume":"58","author":"AB Arrieta","year":"2020","unstructured":"Arrieta, A.B., et al.: Explainable artificial intelligence (xai): concepts, taxonomies, opportunities and challenges toward responsible AI. Inf. Fusion 58, 82\u2013115 (2020)","journal-title":"Inf. Fusion"},{"issue":"7","key":"22_CR3","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0130140","volume":"10","author":"S Bach","year":"2015","unstructured":"Bach, S., Binder, A., Montavon, G., Klauschen, F., M\u00fcller, K.R., Samek, W.: On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation. PloS One 10(7), e0130140 (2015)","journal-title":"PloS One"},{"key":"22_CR4","doi-asserted-by":"crossref","unstructured":"Baltrusaitis, T., Zadeh, A., Lim, Y.C., Morency, L.P.: Openface 2.0: facial behavior analysis toolkit. In: 2018 13th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2018), pp. 59\u201366. IEEE (2018)","DOI":"10.1109\/FG.2018.00019"},{"issue":"7","key":"22_CR5","first-page":"1589","volume":"41","author":"AK Dubey","year":"2020","unstructured":"Dubey, A.K., Jain, V.: Automatic facial recognition using vgg16 based transfer learning model. J. Inf. Optim. Sci. 41(7), 1589\u20131596 (2020)","journal-title":"J. Inf. Optim. Sci."},{"key":"22_CR6","doi-asserted-by":"crossref","unstructured":"Ekman, P., Friesen, W.V.: Facial action coding system. Environ. Psychol. Nonverbal Behav. (1978)","DOI":"10.1037\/t27734-000"},{"key":"22_CR7","unstructured":"Ekman, P., Friesen, W.V., Ellsworth, P.: Emotion in the human face: Guidelines for research and an integration of findings, vol. 11. Elsevier (2013)"},{"key":"22_CR8","doi-asserted-by":"publisher","first-page":"228","DOI":"10.1016\/j.patrec.2021.06.030","volume":"150","author":"M Ivanovs","year":"2021","unstructured":"Ivanovs, M., Kadikis, R., Ozols, K.: Perturbation-based methods for explaining deep neural networks: a survey. Pattern Recogn. Lett. 150, 228\u2013234 (2021)","journal-title":"Pattern Recogn. Lett."},{"key":"22_CR9","doi-asserted-by":"crossref","unstructured":"Kavila, S.D., Bandaru, R., Gali, T.V.M.B., Shafi, J.: Analysis of cardiovascular disease prediction using model-agnostic explainable artificial intelligence techniques. In: Principles and Methods of Explainable Artificial Intelligence in Healthcare, pp. 27\u201354. IGI Global (2022)","DOI":"10.4018\/978-1-6684-3791-9.ch002"},{"key":"22_CR10","doi-asserted-by":"crossref","unstructured":"Lien, J.J., Kanade, T., Cohn, J.F., Li, C.C.: Automated facial expression recognition based on facs action units. In: Proceedings Third IEEE International Conference on Automatic Face and Gesture Recognition, pp. 390\u2013395. IEEE (1998)","DOI":"10.1109\/AFGR.1998.670980"},{"key":"22_CR11","doi-asserted-by":"crossref","unstructured":"Lundqvist, D., Flykt, A., \u00d6hman, A.: Karolinska directed emotional faces. Cogn. Emot. (1998)","DOI":"10.1037\/t27732-000"},{"key":"22_CR12","doi-asserted-by":"crossref","unstructured":"Malik, S., Kumar, P., Raman, B.: Towards interpretable facial emotion recognition. In: Proceedings of the Twelfth Indian Conference on Computer Vision, Graphics and Image Processing, pp. 1\u20139 (2021)","DOI":"10.1145\/3490035.3490271"},{"key":"22_CR13","doi-asserted-by":"publisher","first-page":"120","DOI":"10.3389\/fnagi.2018.00120","volume":"10","author":"M Martinez","year":"2018","unstructured":"Martinez, M., et al.: Emotion detection deficits and decreased empathy in patients with alzheimer\u2019s disease and parkinson\u2019s disease affect caregiver mood and burden. Front. Aging Neurosci. 10, 120 (2018)","journal-title":"Front. Aging Neurosci."},{"key":"22_CR14","doi-asserted-by":"crossref","unstructured":"Montavon, G., Binder, A., Lapuschkin, S., Samek, W., M\u00fcller, K.R.: Layer-wise relevance propagation: an overview. In: Explainable AI: Interpreting, Explaining and Visualizing Deep Learning, pp. 193\u2013209 (2019)","DOI":"10.1007\/978-3-030-28954-6_10"},{"key":"22_CR15","unstructured":"Nie, W., Zhang, Y., Patel, A.: A theoretical explanation for perplexing behaviors of backpropagation-based visualizations. In: International Conference on Machine Learning, pp. 3809\u20133818. PMLR (2018)"},{"key":"22_CR16","doi-asserted-by":"crossref","unstructured":"Rathod, J., Joshi, C., Khochare, J., Kazi, F.: Interpreting a black-box model used for scada attack detection in gas pipelines control system. In: 2020 IEEE 17th India Council International Conference (INDICON), pp. 1\u20137. IEEE (2020)","DOI":"10.1109\/INDICON49873.2020.9342087"},{"key":"22_CR17","doi-asserted-by":"crossref","unstructured":"Ribeiro, M.T., Singh, S., Guestrin, C.: \u201cwhy should i trust you?\" explaining the predictions of any classifier. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1135\u20131144 (2016)","DOI":"10.1145\/2939672.2939778"},{"key":"22_CR18","series-title":"Human\u2013Computer Interaction Series","doi-asserted-by":"publisher","first-page":"159","DOI":"10.1007\/978-3-319-90403-0_9","volume-title":"Human and Machine Learning","author":"M Robnik-\u0160ikonja","year":"2018","unstructured":"Robnik-\u0160ikonja, M., Bohanec, M.: Perturbation-based explanations of prediction models. In: Zhou, J., Chen, F. (eds.) Human and Machine Learning. HIS, pp. 159\u2013175. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-319-90403-0_9"},{"key":"22_CR19","volume-title":"What the Face Reveals: Basic and Applied Studies of Spontaneous Expression Using the Facial Action Coding System (FACS)","author":"EL Rosenberg","year":"2020","unstructured":"Rosenberg, E.L., Ekman, P.: What the Face Reveals: Basic and Applied Studies of Spontaneous Expression Using the Facial Action Coding System (FACS). Oxford University Press, Oxford (2020)"},{"key":"22_CR20","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-28954-6","volume-title":"Explainable AI: Interpreting, Explaining and Visualizing Deep Learning","year":"2019","unstructured":"Samek, W., Montavon, G., Vedaldi, A., Hansen, L.K., M\u00fcller, K.-R. (eds.): Explainable AI: Interpreting, Explaining and Visualizing Deep Learning. LNCS (LNAI), vol. 11700. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-28954-6"},{"issue":"1","key":"22_CR21","doi-asserted-by":"publisher","first-page":"93","DOI":"10.1007\/s12369-017-0433-8","volume":"10","author":"A Taheri","year":"2018","unstructured":"Taheri, A., Meghdari, A., Alemi, M., Pouretemad, H.: Human-robot interaction in autism treatment: a case study on three pairs of autistic children as twins, siblings, and classmates. Int. J. Social Rob. 10(1), 93\u2013113 (2018)","journal-title":"Int. J. Social Rob."},{"issue":"2","key":"22_CR22","doi-asserted-by":"publisher","first-page":"97","DOI":"10.1109\/34.908962","volume":"23","author":"YI Tian","year":"2001","unstructured":"Tian, Y.I., Kanade, T., Cohn, J.F.: Recognizing action units for facial expression analysis. IEEE Trans. Pattern Anal. Mach, Intell. 23(2), 97\u2013115 (2001)","journal-title":"IEEE Trans. Pattern Anal. Mach, Intell."},{"issue":"16","key":"22_CR23","doi-asserted-by":"publisher","first-page":"24287","DOI":"10.1007\/s11042-021-10836-w","volume":"80","author":"L Yao","year":"2021","unstructured":"Yao, L., Wan, Y., Ni, H., Xu, B.: Action unit classification for facial expression recognition using active learning and svm. Multimedia Tools Appl. 80(16), 24287\u201324301 (2021)","journal-title":"Multimedia Tools Appl."},{"key":"22_CR24","doi-asserted-by":"crossref","unstructured":"Yin, P., Huang, L., Lee, S., Qiao, M., Asthana, S., Nakamura, T.: Diagnosis of neural network via backward deduction. In: 2019 IEEE International Conference on Big Data (Big Data), pp. 260\u2013267. IEEE (2019)","DOI":"10.1109\/BigData47090.2019.9006466"},{"key":"22_CR25","unstructured":"Yu, C.: Robot Behavior Generation and Human Behavior Understanding in Natural Human-Robot Interaction. Ph.D. thesis, Institut polytechnique de Paris (2021)"},{"key":"22_CR26","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"633","DOI":"10.1007\/978-3-030-35888-4_59","volume-title":"Social Robotics","author":"C Yu","year":"2019","unstructured":"Yu, C., Tapus, A.: Interactive robot learning for multimodal emotion recognition. In: Salichs, M.A., et al. (eds.) ICSR 2019. LNCS (LNAI), vol. 11876, pp. 633\u2013642. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-35888-4_59"},{"key":"22_CR27","doi-asserted-by":"crossref","unstructured":"Yu, C., Tapus, A.: Multimodal emotion recognition with thermal and rgb-d cameras for human-robot interaction. In: Companion of the 2020 ACM\/IEEE International Conference on Human-Robot Interaction, pp. 532\u2013534 (2020)","DOI":"10.1145\/3371382.3378342"},{"key":"22_CR28","doi-asserted-by":"crossref","unstructured":"Zhang, H., Yu, C., Tapus, A.: Why do you think this joke told by robot is funny? the humor style matters. In: 2022 31st IEEE International Conference on Robot and Human Interactive Communication (RO-MAN), pp. 572\u2013577. IEEE (2022)","DOI":"10.1109\/RO-MAN53752.2022.9900515"}],"container-title":["Lecture Notes in Computer Science","Social Robotics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-24667-8_22","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,13]],"date-time":"2024-10-13T09:32:19Z","timestamp":1728811939000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-24667-8_22"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031246661","9783031246678"],"references-count":28,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-24667-8_22","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"1 February 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICSR","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Social Robotics","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Florence","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Italy","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"13 December 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"16 December 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"14","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"socrob2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.icsr2022.it\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EquinOCS","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"143","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"111","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"78% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}