{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,27]],"date-time":"2026-05-27T13:04:08Z","timestamp":1779887048937,"version":"3.53.1"},"reference-count":75,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2022,5,14]],"date-time":"2022-05-14T00:00:00Z","timestamp":1652486400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"FONDO NACIONAL DE DESARROLLO CIENT\u00cdFICO, TECNOL\u00d3GICO Y DE INNOVACI\u00d3N TECNOL\u00d3GICA\u2014FONDECYT","award":["01-2019-FONDECYT-BM-INC.INV"],"award-info":[{"award-number":["01-2019-FONDECYT-BM-INC.INV"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Social robotics is an emerging area that is becoming present in social spaces, by introducing autonomous social robots. Social robots offer services, perform tasks, and interact with people in such social environments, demanding more efficient and complex Human\u2013Robot Interaction (HRI) designs. A strategy to improve HRI is to provide robots with the capacity of detecting the emotions of the people around them to plan a trajectory, modify their behaviour, and generate an appropriate interaction with people based on the analysed information. However, in social environments in which it is common to find a group of persons, new approaches are needed in order to make robots able to recognise groups of people and the emotion of the groups, which can be also associated with a scene in which the group is participating. Some existing studies are focused on detecting group cohesion and the recognition of group emotions; nevertheless, these works do not focus on performing the recognition tasks from a robocentric perspective, considering the sensory capacity of robots. In this context, a system to recognise scenes in terms of groups of people, to then detect global (prevailing) emotions in a scene, is presented. The approach proposed to visualise and recognise emotions in typical HRI is based on the face size of people recognised by the robot during its navigation (face sizes decrease when the robot moves away from a group of people). On each frame of the video stream of the visual sensor, individual emotions are recognised based on the Visual Geometry Group (VGG) neural network pre-trained to recognise faces (VGGFace); then, to detect the emotion of the frame, individual emotions are aggregated with a fusion method, and consequently, to detect global (prevalent) emotion in the scene (group of people), the emotions of its constituent frames are also aggregated. Additionally, this work proposes a strategy to create datasets with images\/videos in order to validate the estimation of emotions in scenes and personal emotions. Both datasets are generated in a simulated environment based on the Robot Operating System (ROS) from videos captured by robots through their sensory capabilities. Tests are performed in two simulated environments in ROS\/Gazebo: a museum and a cafeteria. Results show that the accuracy in the detection of individual emotions is 99.79% and the detection of group emotion (scene emotion) in each frame is 90.84% and 89.78% in the cafeteria and the museum scenarios, respectively.<\/jats:p>","DOI":"10.3390\/s22103749","type":"journal-article","created":{"date-parts":[[2022,5,15]],"date-time":"2022-05-15T09:48:22Z","timestamp":1652608102000},"page":"3749","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":28,"title":["Group Emotion Detection Based on Social Robot Perception"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9613-6487","authenticated-orcid":false,"given":"Marco","family":"Quiroz","sequence":"first","affiliation":[{"name":"Electrical and Electronics Engineering Department, School of Electronics and Telecommunications Engineering, Universidad Cat\u00f3lica San Pablo, Arequipa 04001, Peru"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Raquel","family":"Pati\u00f1o","sequence":"additional","affiliation":[{"name":"Electrical and Electronics Engineering Department, School of Electronics and Telecommunications Engineering, Universidad Cat\u00f3lica San Pablo, Arequipa 04001, Peru"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8447-784X","authenticated-orcid":false,"given":"Jos\u00e9","family":"Diaz-Amado","sequence":"additional","affiliation":[{"name":"Electrical and Electronics Engineering Department, School of Electronics and Telecommunications Engineering, Universidad Cat\u00f3lica San Pablo, Arequipa 04001, Peru"},{"name":"Instituto Federal da Bahia, Vitoria da Conquista 45078-300, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5966-0113","authenticated-orcid":false,"given":"Yudith","family":"Cardinale","sequence":"additional","affiliation":[{"name":"Electrical and Electronics Engineering Department, School of Electronics and Telecommunications Engineering, Universidad Cat\u00f3lica San Pablo, Arequipa 04001, Peru"},{"name":"Higher School of Engineering, Science and Technology, Universidad Internacional de Valencia, 46002 Valencia, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,5,14]]},"reference":[{"key":"ref_1","unstructured":"Duffy, B.R., Rooney, C., O\u2019Hare, G.M., and O\u2019Donoghue, R. What is a social robot? In Proceedings of the 10th Irish Conference on Artificial Intelligence & Cognitive Science, Cork, Ireland, 1\u20133 September 1999."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Casas, J., Gomez, N.C., Senft, E., Irfan, B., Guti\u00e9rrez, L.F., Rinc\u00f3n, M., M\u00fanera, M., Belpaeme, T., and Cifuentes, C.A. (2018, January 1\u20133). Architecture for a social assistive robot in cardiac rehabilitation. Proceedings of the Colombian Conference on Robotics and Automation (CCRA), Barranquilla, Colombia.","DOI":"10.1109\/CCRA.2018.8588133"},{"key":"ref_3","unstructured":"Cooper, S., Di Fava, A., Vivas, C., Marchionni, L., and Ferro, F. (September, January 31). ARI: The social assistive robot and companion. Proceedings of the International Conference on Robot and Human Interactive Communication (RO-MAN), Naples, Italy."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Nocentini, O., Fiorini, L., Acerbi, G., Sorrentino, A., Mancioppi, G., and Cavallo, F. (2019). A survey of behavioural models for social robots. Robotics, 8.","DOI":"10.20944\/preprints201905.0251.v1"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"5954","DOI":"10.1109\/TCYB.2020.2974688","article-title":"A Multimodal Emotional Human-Robot Interaction Architecture for Social Robots Engaged in Bidirectional Communication","volume":"51","author":"Hong","year":"2020","journal-title":"IEEE Trans. Cybern."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"668","DOI":"10.1109\/JAS.2017.7510622","article-title":"A facial expression emotion recognition based human\u2013robot interaction system","volume":"4","author":"Liu","year":"2017","journal-title":"IEEE\/CAA J. Autom. Sin."},{"key":"ref_7","unstructured":"Lopez-Rincon, A. (March, January 27). Emotion recognition using facial expressions in children using the NAO Robot. Proceedings of the International Conference on Electronics, Communications and Computers (CONIELECOMP), Cholula, Mexico."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1007\/s42235-018-0015-y","article-title":"Emotion modelling for social robotics applications: A review","volume":"15","author":"Cavallo","year":"2018","journal-title":"J. Bionic Eng."},{"key":"ref_9","first-page":"125","article-title":"A Survey on Emotion Recognition for Human Robot Interaction","volume":"28","author":"Mohammed","year":"2020","journal-title":"J. Comput. Inf. Technol."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"104178","DOI":"10.1016\/j.engappai.2021.104178","article-title":"Emotion space modelling for social robots","volume":"100","author":"Yan","year":"2021","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_11","unstructured":"Bandini, A., and Zariffa, J. (2020). Analysis of the hands in egocentric vision: A survey. IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Pathi, S.K., Kiselev, A., and Loutfi, A. (2022). Detecting Groups and Estimating F-Formations for Social Human\u2013Robot Interactions. Multimodal Technol. Interact., 6.","DOI":"10.3390\/mti6030018"},{"key":"ref_13","first-page":"284","article-title":"Social navigation framework for assistive robots in human inhabited unknown environments","volume":"24","author":"Kivrak","year":"2021","journal-title":"Eng. Sci. Technol. Int. J."},{"key":"ref_14","unstructured":"Liu, S., Chang, P., Huang, Z., Chakraborty, N., Liang, W., Geng, J., and Driggs-Campbell, K. (2022). Socially Aware Robot Crowd Navigation with Interaction Graphs and Human Trajectory Prediction. arXiv."},{"key":"ref_15","unstructured":"Bera, A., Randhavane, T., Prinja, R., Kapsaskis, K., Wang, A., Gray, K., and Manocha, D. (2019). The emotionally intelligent robot: Improving social navigation in crowded environments. arXiv."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1008","DOI":"10.1109\/LRA.2021.3135560","article-title":"CoMet: Modeling group cohesion for socially compliant robot navigation in crowded scenes","volume":"7","author":"Sathyamoorthy","year":"2021","journal-title":"Robot. Autom. Lett."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Guo, X., Polan\u00eda, L.F., and Barner, K.E. (2017, January 13\u201317). Group-level emotion recognition using deep models on image scene, faces, and skeletons. Proceedings of the ACM International Conference on Multimodal Interaction, Glasgow, UK.","DOI":"10.1145\/3136755.3143017"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Guo, X., Zhu, B., Polan\u00eda, L.F., Boncelet, C., and Barner, K.E. (2018, January 16\u201320). Group-level emotion recognition using hybrid deep models based on faces, scenes, skeletons and visual attentions. Proceedings of the ACM International Conference on Multimodal Interaction, Boulder, CO, USA.","DOI":"10.1145\/3242969.3264990"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Xuan Dang, T., Kim, S.H., Yang, H.J., Lee, G.S., and Vo, T.H. (2019, January 14\u201318). Group-level Cohesion Prediction using Deep Learning Models with A Multi-stream Hybrid Network. Proceedings of the International Conference on Multimodal Interaction, Suzhou, China.","DOI":"10.1145\/3340555.3355715"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"84356","DOI":"10.1109\/ACCESS.2021.3088340","article-title":"D2C-Based Hybrid Network for Predicting Group Cohesion Scores","volume":"9","author":"Tien","year":"2021","journal-title":"IEEE Access"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"108646","DOI":"10.1016\/j.patcog.2022.108646","article-title":"Non-Volume Preserving-based Fusion to Group-Level Emotion Recognition on Crowd Videos","volume":"128","author":"Quach","year":"2022","journal-title":"Pattern Recognit."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1499","DOI":"10.1109\/LSP.2016.2603342","article-title":"Joint face detection and alignment using multitask cascaded convolutional networks","volume":"23","author":"Zhang","year":"2016","journal-title":"Signal Process. Lett."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Zhu, B., Guo, X., Barner, K., and Boncelet, C. (2019, January 14\u201318). Automatic group cohesiveness detection with multi-modal features. Proceedings of the International Conference on Multimodal Interaction, Suzhou, China.","DOI":"10.1145\/3340555.3355716"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Tan, L., Zhang, K., Wang, K., Zeng, X., Peng, X., and Qiao, Y. (2017, January 13\u201317). Group emotion recognition with individual facial emotion CNNs and global image based CNNs. Proceedings of the ACM International Conference on Multimodal Interaction, Glasgow, UK.","DOI":"10.1145\/3136755.3143008"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Wang, K., Zeng, X., Yang, J., Meng, D., Zhang, K., Peng, X., and Qiao, Y. (2018, January 16\u201320). Cascade attention networks for group emotion recognition with face, body and image cues. Proceedings of the ACM International Conference on Multimodal Interaction, Boulder, CO, USA.","DOI":"10.1145\/3242969.3264991"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Khan, A.S., Li, Z., Cai, J., Meng, Z., O\u2019Reilly, J., and Tong, Y. (2018, January 16\u201320). Group-level emotion recognition using deep models with a four-stream hybrid network. Proceedings of the ACM International Conference on Multimodal Interaction, Boulder, CO, USA.","DOI":"10.1145\/3242969.3264987"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Gupta, A., Agrawal, D., Chauhan, H., Dolz, J., and Pedersoli, M. (2018, January 16\u201320). An attention model for group-level emotion recognition. Proceedings of the ACM International Conference on Multimodal Interaction, Boulder, CO, USA.","DOI":"10.1145\/3242969.3264985"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Guo, X., Polania, L., Zhu, B., Boncelet, C., and Barner, K. (2020, January 1\u20135). Graph neural networks for image understanding based on multiple cues: Group emotion recognition and event recognition as use cases. Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, Snowmass Village, CO, USA.","DOI":"10.1109\/WACV45572.2020.9093547"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Khan, A.S., Li, Z., Cai, J., and Tong, Y. (2021, January 4\u20138). Regional Attention Networks with Context-aware Fusion for Group Emotion Recognition. Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, Waikoloa, HI, USA.","DOI":"10.1109\/WACV48630.2021.00119"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Sun, M., Li, J., Feng, H., Gou, W., Shen, H., Tang, J., Yang, Y., and Ye, J. (2020, January 25\u201329). Multi-Modal Fusion Using Spatio-Temporal and Static Features for Group Emotion Recognition. Proceedings of the International Conference on Multimodal Interaction, Online.","DOI":"10.1145\/3382507.3417971"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Balaji, B., and Oruganti, V.R.M. (2017, January 13\u201317). Multi-level feature fusion for group-level emotion recognition. Proceedings of the ACM International Conference on Multimodal Interaction, Glasgow, UK.","DOI":"10.1145\/3136755.3143013"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Guo, D., Wang, K., Yang, J., Zhang, K., Peng, X., and Qiao, Y. (2019, January 14\u201318). Exploring Regularizations with Face, Body and Image Cues for Group Cohesion Prediction. Proceedings of the International Conference on Multimodal Interaction, Suzhou, China.","DOI":"10.1145\/3340555.3355712"},{"key":"ref_33","unstructured":"Viola, P., and Jones, M. (2001, January 8\u201314). Rapid object detection using a boosted cascade of simple features. Proceedings of the Computer Society Conference on Computer Vision and Pattern Recognition, Kauai, HI, USA."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Rassadin, A., Gruzdev, A., and Savchenko, A. (2017, January 13\u201317). Group-level emotion recognition using transfer learning from face identification. Proceedings of the ACM International Conference on Multimodal Interaction, Glasgow, UK.","DOI":"10.1145\/3136755.3143007"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Wei, Q., Zhao, Y., Xu, Q., Li, L., He, J., Yu, L., and Sun, B. (2017, January 13\u201317). A new deep-learning framework for group emotion recognition. Proceedings of the ACM International Conference on Multimodal Interaction, Glasgow, UK.","DOI":"10.1145\/3136755.3143014"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Sun, B., Wei, Q., Li, L., Xu, Q., He, J., and Yu, L. (2016, January 12\u201316). LSTM for dynamic emotion and group emotion recognition in the wild. Proceedings of the ACM International Conference on Multimodal Interaction, Tokyo, Japan.","DOI":"10.1145\/2993148.2997640"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Abbas, A., and Chalup, S.K. (2017, January 13\u201317). Group emotion recognition in the wild by combining deep neural networks for facial expression classification and scene-context analysis. Proceedings of the ACM International Conference on Multimodal Interaction, Glasgow, UK.","DOI":"10.1145\/3136755.3143010"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Hassner, T., Harel, S., Paz, E., and Enbar, R. (2015, January 7\u201312). Effective face frontalization in unconstrained images. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7299058"},{"key":"ref_39","unstructured":"Simonyan, K., and Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"111617","DOI":"10.1109\/ACCESS.2019.2932797","article-title":"Group emotion recognition based on global and local features","volume":"7","author":"Yu","year":"2019","journal-title":"IEEE Access"},{"key":"ref_41","unstructured":"Savery, R., and Weinberg, G. (September, January 31). A Survey of Robotics and Emotion: Classifications and Models of Emotional Interaction. Proceedings of the International Conference on Robot and Human Interactive Communication (RO-MAN), Naples, Italy."},{"key":"ref_42","first-page":"1","article-title":"Survey of Emotions in Human\u2013Robot Interactions: Perspectives from Robotic Psychology on 20 Years of Research","volume":"14","year":"2021","journal-title":"Int. J. Soc. Rob."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Bhagya, S., Samarakoon, P., Viraj, M., Muthugala, J., Buddhika, A., Jayasekara, P., and Elara, M.R. (2019, January 14\u201318). An exploratory study on proxemics preferences of humans in accordance with attributes of service robots. Proceedings of the International Conference on Robot and Human Interactive Communication (RO-MAN), New Delhi, India.","DOI":"10.1109\/RO-MAN46459.2019.8956297"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Gin\u00e9s, J., Mart\u00edn, F., Vargas, D., Rodr\u00edguez, F.J., and Matell\u00e1n, V. (2019). Social navigation in a cognitive architecture using dynamic proxemic zones. Sensors, 19.","DOI":"10.3390\/s19235189"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Rawal, N., and Stock-Homburg, R.M. (2021). Facial emotion expressions in human\u2013robot interaction: A survey. arXiv.","DOI":"10.1007\/s12369-022-00867-0"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Yu, C., and Tapus, A. (2019). Interactive Robot Learning for Multimodal Emotion Recognition. Social Robotics, Springer.","DOI":"10.1007\/978-3-030-35888-4_59"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Kashii, A., Takashio, K., and Tokuda, H. (2017, January 28\u201331). Ex-amp robot: Expressive robotic avatar with multimodal emotion detection to enhance communication of users with motor disabilities. Proceedings of the 26th International Symposium on Robot and Human Interactive Communication (RO-MAN), Lisbon, Portugal.","DOI":"10.1109\/ROMAN.2017.8172404"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Lui, J.H., Samani, H., and Tien, K.Y. (2017, January 13\u201316). An affective mood booster robot based on emotional processing unit. Proceedings of the International Automatic Control Conference (CACS), Keelung, Taiwan.","DOI":"10.1109\/CACS.2017.8284239"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"5073","DOI":"10.1007\/s11042-016-3797-0","article-title":"Simulating empathic behaviour in a social assistive robot","volume":"76","author":"Ferilli","year":"2017","journal-title":"Multimed. Tools Appl."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Castillo, J.C., Castro-Gonz\u00e1lez, \u00c1., Alonso-Mart\u00edn, F., Fern\u00e1ndez-Caballero, A., and Salichs, M.\u00c1. (2018). Emotion detection and regulation from personal assistant robot in smart environment. Personal Assistants: Emerging Computational Technologies, Springer.","DOI":"10.1007\/978-3-319-62530-0_10"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Adiga, S., Vaishnavi, D.V., Saxena, S., and Tripathi, S. (2020, January 14\u201315). Multimodal Emotion Recognition for Human Robot Interaction. Proceedings of the 7th International Conference on Soft Computing & Machine Intelligence (ISCMI), Stockholm, Sweden.","DOI":"10.1109\/ISCMI51676.2020.9311566"},{"key":"ref_52","first-page":"255","article-title":"Multimodal Emotion Recognition and Intention Understanding in Human-Robot Interaction","volume":"329","author":"Chen","year":"2021","journal-title":"Dev. Adv. Control. Intell. Autom. Complex Syst."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"20727","DOI":"10.1109\/ACCESS.2022.3149214","article-title":"Adaptive Multimodal Emotion Detection Architecture for Social Robots","volume":"10","author":"Heredia","year":"2022","journal-title":"IEEE Access"},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Graterol, W., Diaz-Amado, J., Cardinale, Y., Dongo, I., Lopes-Silva, E., and Santos-Libarino, C. (2021). Emotion Detection for Social Robots Based on NLP Transformers and an Emotion Ontology. Sensors, 21.","DOI":"10.3390\/s21041322"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"532279","DOI":"10.3389\/frobt.2020.532279","article-title":"Emotion Recognition for Human-Robot Interaction: Recent Advances and Future Perspectives","volume":"7","author":"Spezialetti","year":"2020","journal-title":"Front. Robot. AI"},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Du, Y., Hetherington, N.J., Oon, C.L., Chan, W.P., Quintero, C.P., Croft, E., and Van der Loos, H.M. (2019, January 20\u201324). Group surfing: A pedestrian-based approach to sidewalk robot navigation. Proceedings of the International Conference on Robotics and Automation (ICRA), Montreal, QC, Canada.","DOI":"10.1109\/ICRA.2019.8793608"},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Yang, F., and Peters, C. (2019, January 14\u201318). Appgan: Generative adversarial networks for generating robot approach behaviours into small groups of people. Proceedings of the International Conference on Robot and Human Interactive Communication (RO-MAN), New Delhi, India.","DOI":"10.1109\/RO-MAN46459.2019.8956425"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3375798","article-title":"Robot-centric perception of human groups","volume":"9","author":"Taylor","year":"2020","journal-title":"ACM Trans. Hum.-Robot Interact."},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"V\u00e1zquez, M., Carter, E.J., McDorman, B., Forlizzi, J., Steinfeld, A., and Hudson, S.E. (2017, January 6\u20139). Towards robot autonomy in group conversations: Understanding the effects of body orientation and gaze. Proceedings of the ACM\/IEEE International Conference on Human-Robot Interaction (HRI), Vienna, Austria.","DOI":"10.1145\/2909824.3020207"},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Hayamizu, T., Mutsuo, S., Miyawaki, K., Mori, H., Nishiguchi, S., and Yamashita, N. (2012, January 23\u201325). Group emotion estimation using Bayesian network based on facial expression and prosodic information. Proceedings of the International Conference on Control System, Computing and Engineering, Penang, Malaysia.","DOI":"10.1109\/ICCSCE.2012.6487137"},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"355","DOI":"10.1016\/j.neucom.2018.09.109","article-title":"Bayesian networks+ reinforcement learning: Controlling group emotion from sensory stimuli","volume":"391","author":"Choi","year":"2020","journal-title":"Neurocomputing"},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Cosentino, S., Randria, E.I., Lin, J.Y., Pellegrini, T., Sessa, S., and Takanishi, A. (2018, January 1\u20135). Group emotion recognition strategies for entertainment robots. Proceedings of the IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Madrid, Spain.","DOI":"10.1109\/IROS.2018.8593503"},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Oliveira, R., Arriaga, P., and Paiva, A. (2021). Human-robot interaction in groups: Methodological and research practices. Multimodal Technol. Interact., 5.","DOI":"10.3390\/mti5100059"},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Schmuck, V., and Celiktutan, O. (2020, January 14\u201317). RICA: Robocentric Indoor Crowd Analysis Dataset. Proceedings of the Conference for PhD Students & Early Career Researcher, Lincoln, UK.","DOI":"10.31256\/Io1Sq2R"},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"169","DOI":"10.1080\/02699939208411068","article-title":"An argument for basic emotions","volume":"6","author":"Ekman","year":"1992","journal-title":"Cogn. Emot."},{"key":"ref_66","first-page":"197","article-title":"Emotions: A general psychoevolutionary theory","volume":"1984","author":"Plutchik","year":"1984","journal-title":"Approaches Emot."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1080\/02699938908408075","article-title":"The language of emotions: An analysis of a semantic field","volume":"3","author":"Oatley","year":"1989","journal-title":"Cogn. Emot."},{"key":"ref_68","unstructured":"Schmuck, V., Sheng, T., and Celiktutan, O. (September, January 31). Robocentric Conversational Group Discovery. Proceedings of the International Conference on Robot and Human Interactive Communication (RO-MAN), Naples, Italy."},{"key":"ref_69","doi-asserted-by":"crossref","unstructured":"Schmuck, V., and Celiktutan, O. (2021, January 15\u201318). GROWL: Group Detection With Link Prediction. Proceedings of the International Conference on Automatic Face and Gesture Recognition, Jodhpur, India.","DOI":"10.1109\/FG52635.2021.9667061"},{"key":"ref_70","doi-asserted-by":"crossref","unstructured":"Taylor, A., and Riek, L.D. (2022, January 7\u201310). REGROUP: A Robot-Centric Group Detection and Tracking System. Proceedings of the ACM\/IEEE International Conference on Human-Robot Interaction, Hokkaido, Japan.","DOI":"10.1109\/HRI53351.2022.9889634"},{"key":"ref_71","doi-asserted-by":"crossref","unstructured":"Azagra, P., Golemo, F., Mollard, Y., Lopes, M., Civera, J., and Murillo, A.C. (2017, January 24\u201328). A multimodal dataset for object model learning from natural human\u2013robot interaction. Proceedings of the IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Vancouver, BC, Canada.","DOI":"10.1109\/IROS.2017.8206514"},{"key":"ref_72","unstructured":"Bloesch, M., Omari, S., Hutter, M., and Siegwart, R. (October, January 28). Robust visual inertial odometry using a direct EKF-based approach. Proceedings of the IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Hamburg, Germany."},{"key":"ref_73","doi-asserted-by":"crossref","unstructured":"Huai, Z., and Huang, G. (2018, January 1\u20135). Robocentric visual-inertial odometry. Proceedings of the IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Madrid, Spain.","DOI":"10.1109\/IROS.2018.8593643"},{"key":"ref_74","doi-asserted-by":"crossref","unstructured":"Wagstaff, B., Wise, E., and Kelly, J. (2022). A Self-Supervised, Differentiable Kalman Filter for Uncertainty-Aware Visual-Inertial Odometry. arXiv.","DOI":"10.1109\/AIM52237.2022.9863270"},{"key":"ref_75","doi-asserted-by":"crossref","unstructured":"Heredia, J., Cardinale, Y., Dongo, I., and D\u00edaz-Amado, J. (2021, January 6\u20138). A multi-modal visual emotion recognition method to instantiate an ontology. Proceedings of the 16th International Conference on Software Technologies, Online.","DOI":"10.5220\/0010516104530464"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/10\/3749\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T23:10:51Z","timestamp":1760137851000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/10\/3749"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,5,14]]},"references-count":75,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2022,5]]}},"alternative-id":["s22103749"],"URL":"https:\/\/doi.org\/10.3390\/s22103749","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,5,14]]}}}