{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,13]],"date-time":"2026-03-13T08:58:04Z","timestamp":1773392284567,"version":"3.50.1"},"reference-count":80,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2022,2,23]],"date-time":"2022-02-23T00:00:00Z","timestamp":1645574400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["MTI"],"abstract":"<jats:p>The ability of a robot to detect and join groups of people is of increasing importance in social contexts, and for the collaboration between teams of humans and robots. In this paper, we propose a framework, autonomous group interactions for robots (AGIR), that endows a robot with the ability to detect such groups while following the principles of F-formations. Using on-board sensors, this method accounts for a wide spectrum of different robot systems, ranging from autonomous service robots to telepresence robots. The presented framework detects individuals, estimates their position and orientation, detects groups, determines their F-formations, and is able to suggest a position for the robot to enter the social group. For evaluation, two simulation scenes were developed based on the standard real-world datasets. The 1st scene is built with 20 virtual agents (VAs) interacting in 7 different groups of varying sizes and 3 different formations. The 2nd scene is built with 36 VAs, positioned in 13 different groups of varying sizes and 6 different formations. A model of a Pepper robot is used in both simulated scenes in randomly generated different positions. The ability for the robot to estimate orientation, detect groups, and estimate F-formations at various locations is used to determine the validation of the approaches. The obtained results show a high accuracy within each of the simulated scenarios and demonstrates that the framework is able to work from an egocentric view with a robot in real time.<\/jats:p>","DOI":"10.3390\/mti6030018","type":"journal-article","created":{"date-parts":[[2022,2,23]],"date-time":"2022-02-23T22:54:02Z","timestamp":1645656842000},"page":"18","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Detecting Groups and Estimating F-Formations for Social Human\u2013Robot Interactions"],"prefix":"10.3390","volume":"6","author":[{"given":"Sai Krishna","family":"Pathi","sequence":"first","affiliation":[{"name":"Center for Applied Autonomous Sensor Systems (AASS), School of Science and Technology, \u00d6rebro University, 701 82 \u00d6rebro, Sweden"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0305-3728","authenticated-orcid":false,"given":"Andrey","family":"Kiselev","sequence":"additional","affiliation":[{"name":"Center for Applied Autonomous Sensor Systems (AASS), School of Science and Technology, \u00d6rebro University, 701 82 \u00d6rebro, Sweden"}]},{"given":"Amy","family":"Loutfi","sequence":"additional","affiliation":[{"name":"Center for Applied Autonomous Sensor Systems (AASS), School of Science and Technology, \u00d6rebro University, 701 82 \u00d6rebro, Sweden"}]}],"member":"1968","published-online":{"date-parts":[[2022,2,23]]},"reference":[{"key":"ref_1","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_2","unstructured":"V\u00e1zquez, M. (2017). Reasoning about Spatial Patterns of Human Behavior during Group Conversations with Robots. [Ph.D. Thesis, Carnegie Mellon University]."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Satake, S., Kanda, T., Glas, D.F., Imai, M., Ishiguro, H., and Hagita, N. (2009, January 11\u201313). How to approach humans? Strategies for social robots to initiate interaction. Proceedings of the 4th ACM\/IEEE International Conference on Human Robot Interaction, San Diego, CA, USA.","DOI":"10.1145\/1514095.1514117"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"508","DOI":"10.1109\/TRO.2012.2226387","article-title":"A robot that approaches pedestrians","volume":"29","author":"Satake","year":"2012","journal-title":"IEEE Trans. Robot."},{"key":"ref_5","unstructured":"Walters, M.L., Dautenhahn, K., TeBoekhorst, R., Koay, K.L., Syrdal, D.S., and Nehaniv, C.L. (2009, January 8\u20139). An empirical framework for human\u2013robot proxemics. Proceedings of the Symposium on New Frontiers in Human-Robot Interaction, AISB2009, Edinburgh, Scotland."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Cristani, M., Bazzani, L., Paggetti, G., Fossati, A., Tosato, D., Del Bue, A., Menegaz, G., and Murino, V. (September, January 29). Social interaction discovery by statistical analysis of F-formations. Proceedings of the BMVC 2011\u2014Proceedings of the British Machine Vision Conference 2011, Dundee, UK.","DOI":"10.5244\/C.25.23"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Setti, F., Russell, C., Bassetti, C., and Cristani, M. (2015). F-formation detection: Individuating free-standing conversational groups in images. PLoS ONE, 10.","DOI":"10.1371\/journal.pone.0139160"},{"key":"ref_8","unstructured":"Vascon, S., Mequanint, E.Z., Cristani, M., Hung, H., Pelillo, M., and Murino, V. (2014). A Game-Theoretic Probabilistic Approach for Detecting Conversational Groups. Asian Conference on Computer Vision, Springer."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Correia, F., Alves-Oliveira, P., Maia, N., Ribeiro, T., Petisca, S., Melo, F.S., and Paiva, A. (2016, January 26\u201331). Just follow the suit! trust in human\u2013robot interactions during card game playing. Proceedings of the 2016 25th IEEE International Symposium on Robot and Human Interactive Communication (RO\u2013MAN), New York, NY, USA.","DOI":"10.1109\/ROMAN.2016.7745165"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Oliveira, R., Arriaga, P., Correia, F., and Paiva, A. (2019, January 11\u201314). The stereotype content model applied to human\u2013robot interactions in groups. Proceedings of the 2019 14th ACM\/IEEE International Conference on Human-Robot Interaction (HRI), Daegu, Korea.","DOI":"10.1109\/HRI.2019.8673171"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"291","DOI":"10.1007\/s12369-013-0178-y","article-title":"Social robots for long-term interaction: A survey","volume":"5","author":"Leite","year":"2013","journal-title":"Int. J. Soc. Robot."},{"key":"ref_12","unstructured":"Flickner, M.D., and Haritaoglu, R.I. (2010). Method of Detecting and Tracking Groups of People. (No. 7,688,349), U.S. Patent."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Lau, B., Arras, K.O., and Burgard, W. (2009, January 12\u201317). Tracking groups of people with a multi-model hypothesis tracker. Proceedings of the 2009 IEEE International Conference on Robotics and Automation, Kobe, Japan.","DOI":"10.1109\/ROBOT.2009.5152731"},{"key":"ref_14","unstructured":"Linder, T., and Arras, K.O. (2014, January 7\u201310). Multi-model hypothesis tracking of groups of people in RGB-D data. Proceedings of the 17th International Conference on Information Fusion (FUSION), Salamanca, Spain."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Luber, M., and Arras, K.O. (2013). Multi-hypothesis social grouping and tracking for mobile robots. Robotics: Science and Systems, Springer.","DOI":"10.15607\/RSS.2013.IX.001"},{"key":"ref_16","unstructured":"Hall, E.T. (1966). The Hidden Dimension, Doubleday."},{"key":"ref_17","unstructured":"Kendon, A. (1990). Conducting Interaction: Patterns of Behavior in Focused Encounters, CUP Archive."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Kendon, A. (2010). Spacing and Orientation in Co-Present Interaction, Springer.","DOI":"10.1007\/978-3-642-12397-9_1"},{"key":"ref_19","unstructured":"Swofford, M., Peruzzi, J., and V\u00e1zquez, M. (2018). Conversational group detection with deep convolutional networks. arXiv."},{"key":"ref_20","unstructured":"Taylor, A., and Riek, L.D. (2016). Robot Perception of Human Groups in the Real World: State of the Art. AAAI Fall Symposia Series, AAAI."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Br\u0161\u010di\u0107, D., Zanlungo, F., and Kanda, T. (2017, January 22\u201326). Modelling of pedestrian groups and application to group recognition. Proceedings of the 2017 40th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), Opatija, Croatia.","DOI":"10.23919\/MIPRO.2017.7973489"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Caine, K., \u0160abanovic, S., and Carter, M. (2012, January 5). The effect of monitoring by cameras and robots on the privacy enhancing behaviors of older adults. Proceedings of the Seventh Annual ACM\/IEEE International Conference on Human-Robot Interaction, New York, NY, USA.","DOI":"10.1145\/2157689.2157807"},{"key":"ref_23","first-page":"983","article-title":"Averting robot eyes","volume":"76","author":"Kaminski","year":"2016","journal-title":"Md. L. Rev."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Mazzon, R., Poiesi, F., and Cavallaro, A. (2013, January 27\u201330). Detection and tracking of groups in crowd. Proceedings of the 2013 10th IEEE International Conference on Advanced Video and Signal Based Surveillance, Krakow, Poland.","DOI":"10.1109\/AVSS.2013.6636640"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Ram\u00edrez, O.A.I., Varni, G., Andries, M., Chetouani, M., and Chatila, R. (2016, January 26\u201331). Modeling the dynamics of individual behaviors for group detection in crowds using low-level features. Proceedings of the 2016 25th IEEE International Symposium on Robot and Human Interactive Communication (RO\u2013MAN), New York, NY, USA.","DOI":"10.1109\/ROMAN.2016.7745246"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Pathi, S.K., Kiselev, A., and Loutfi, A. (2017, January 6\u20139). Estimating F-formations for mobile robotic telepresence. Proceedings of the ACM\/IEEE International Conference on Human\u2013Robot Interaction, Vienna, Austria.","DOI":"10.1145\/3029798.3038304"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Pathi, S.K., Kristofferson, A., Kiselev, A., and Loutfi, A. (2019, January 14\u201318). Estimating Optimal Placement for a Robot in Social Group Interaction. Proceedings of the 2019 28th IEEE International Conference on Robot and Human Interactive Communication, RO\u2013MAN, New Delhi, India.","DOI":"10.1109\/RO-MAN46459.2019.8956318"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Barua, H.B., Pramanick, P., Sarkar, C., and Mg, T.H. (2020). Let me join you! Real-time F-formation recognition by a socially aware robot. arXiv.","DOI":"10.1109\/RO-MAN47096.2020.9223469"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"188","DOI":"10.1016\/j.robot.2016.05.004","article-title":"Service robots: System design for tracking people through data fusion and initiating interaction with the human group by inferring social situations","volume":"83","author":"Tseng","year":"2016","journal-title":"Robot. Auton. Syst."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"351","DOI":"10.1177\/001872676802100403","article-title":"Spatial factors in social interactions","volume":"21","author":"Patterson","year":"1968","journal-title":"Hum. Relat."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Walters, M.L., Syrdal, D.S., Koay, K.L., Dautenhahn, K., and TeBoekhorst, R. (2008, January 1\u20133). Human approach distances to a mechanical-looking robot with different robot voice styles. Proceedings of the RO-MAN 2008\u2014The 17th IEEE International Symposium on Robot and Human Interactive Communication, Munich, Germany.","DOI":"10.1109\/ROMAN.2008.4600750"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"151","DOI":"10.1207\/s15327051hci1901&2_7","article-title":"Whose job is it anyway? A study of human\u2013robot interaction in a collaborative task","volume":"19","author":"Hinds","year":"2004","journal-title":"Hum.-Comput. Interact."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Friedman, B., Kahn, P.H., and Hagman, J. (2003, January 5\u201310). Hardware companions? What online AIBO discussion forums reveal about the human\u2013robotic relationship. Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, Ft. Lauderdale, FL, USA.","DOI":"10.1145\/642611.642660"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1086\/200975","article-title":"Proxemics [and comments and replies]","volume":"9","author":"Hall","year":"1968","journal-title":"Curr. Anthropol."},{"key":"ref_35","unstructured":"Sommer, R. (1969). Personal Space. The Behavioral Basis of Design, Prentice Hall."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Marshall, P., Rogers, Y., and Pantidi, N. (2011, January 19\u201323). Using F-formations to analyse spatial patterns of interaction in physical environments. Proceedings of the ACM Conference on Computer Supported Cooperative Work, CSCW, Hangzhou, China.","DOI":"10.1145\/1958824.1958893"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Serna, A., Pageaud, S., Tong, L., George, S., and Tabard, A. (2016, January 6\u20139). F-formations and collaboration dynamics study for designing mobile collocation. Proceedings of the 18th International Conference on Human\u2013Computer Interaction with Mobile Devices and Services Adjunct, MobileHCI 2016, Florence, Italy. Available online: http:\/\/dl.acm.org\/citation.cfm?doid=2957265.2962656.","DOI":"10.1145\/2957265.2962656"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"607","DOI":"10.1109\/TPAMI.2008.106","article-title":"Head pose estimation in computer vision: A survey","volume":"31","author":"Trivedi","year":"2009","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Alletto, S., Serra, G., Calderara, S., and Cucchiara, R. (2014, January 24\u201328). Head pose estimation in first-person camera views. Proceedings of the International Conference on Pattern Recognition, Stockholm, Sweden.","DOI":"10.1109\/ICPR.2014.718"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Robertson, N., and Reid, I. (2006). Estimating gaze direction from low-resolution faces in video. European Conference on Computer Vision, Springer.","DOI":"10.1007\/11744047_31"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"1972","DOI":"10.1109\/TPAMI.2012.263","article-title":"Characterizing humans on riemannian manifolds","volume":"35","author":"Tosato","year":"2012","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Tosato, D., Farenzena, M., Spera, M., Murino, V., and Cristani, M. (2010). Multi-class classification on riemannian manifolds for video surveillance. European Conference on Computer Vision, Springer.","DOI":"10.1007\/978-3-642-15552-9_28"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Raytchev, B., Yoda, I., and Sakaue, K. (2004, January 26). Head pose estimation by nonlinear manifold learning. Proceedings of the International Conference on Pattern Recognition, Cambridge, UK.","DOI":"10.1109\/ICPR.2004.1333802"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Fanelli, G., Gall, J., and Van Gool, L. (2011, January 20\u201325). Real time head pose estimation with random regression forests. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Colorado Springs, CO, USA.","DOI":"10.1109\/CVPR.2011.5995458"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Ruiz, N., Chong, E., and Rehg, J.M. (2018, January 18\u201322). Fine-grained head pose estimation without keypoints. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPRW.2018.00281"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"1245","DOI":"10.1016\/j.cviu.2012.11.005","article-title":"Hough-based tracking of non-rigid objects","volume":"117","author":"Godec","year":"2012","journal-title":"Comput. Vis. Image Underst."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"309","DOI":"10.1145\/1015706.1015720","article-title":"\u201cGrabcut\u201d: Interactive foreground extraction using iterated graph cuts","volume":"23","author":"Rother","year":"2004","journal-title":"ACM Trans. Graph."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"4082","DOI":"10.1016\/j.patcog.2015.06.006","article-title":"Understanding social relationships in egocentric vision","volume":"48","author":"Alletto","year":"2015","journal-title":"Pattern Recognit."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Katevas, K., Haddadi, H., Tokarchuk, L., and Clegg, R.G. (2016, January 12\u201316). Detecting group formations using iBeacon technology. Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct, Heidelberg, Germany.","DOI":"10.1145\/2968219.2968281"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Hung, H., Englebienne, G., and CabreraQuiros, L. (2014, January 12\u201316). Detecting conversing groups with a single worn accelerometer. Proceedings of the 16th International Conference on Multimodal Interaction, Istanbul, Turkey.","DOI":"10.1145\/2663204.2663228"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Tao, Y., Mitsven, S.G., Perry, L.K., Messinger, D.S., and Shyu, M.L. (2019, January 8\u201311). Audio-Based Group Detection for Classroom Dynamics Analysis. Proceedings of the 2019 International Conference on Data Mining Workshops (ICDMW), Beijing, China.","DOI":"10.1109\/ICDMW.2019.00125"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"995","DOI":"10.1109\/TPAMI.2015.2470658","article-title":"Socially constrained structural learning for groups detection in crowd","volume":"38","author":"Solera","year":"2015","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_53","unstructured":"Fern, O.T., Denman, S., Sridharan, S., and Fookes, C. (2018). Gd-gan: Generative adversarial networks for trajectory prediction and group detection in crowds. Asian Conference on Computer Vision, Springer."},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Hung, H., and Kr\u00f6se, B. (2011, January 14\u201318). Detecting F-formations as dominant sets. Proceedings of the ICMI\u201911-Proceedings of the 2011 ACM International Conference on Multimodal Interaction, Alicante, Spain.","DOI":"10.1145\/2070481.2070525"},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Setti, F., Lanz, O., Ferrario, R., Murino, V., and Cristani, M. (2013, January 15\u201318). Multi-scale f-formation discovery for group detection. Proceedings of the 2013 IEEE International Conference on Image Processing, ICIP 2013-Proceedings, Melbourne, VIC, Australia.","DOI":"10.1109\/ICIP.2013.6738732"},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Ricci, E., Varadarajan, J., Subramanian, R., Bulo, S.R., Ahuja, N., and Lanz, O. (2015, January 7\u201315). Uncovering interactions and interactors: Joint estimation of head, body orientation and f-formations from surveillance videos. Proceedings of the IEEE International Conference on Computer Vision (ICCV), Santiago, Chile.","DOI":"10.1109\/ICCV.2015.529"},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Zhang, L., and Hung, H. (2016, January 27\u201330). Beyond F-formations: Determining social involvement in free standing conversing groups from static images. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.123"},{"key":"ref_58","unstructured":"Vazquez, M., Steinfeld, A., and Hudson, S.E. (October, January 28). Parallel detection of conversational groups of free-standing people and tracking of their lower-body orientation. Proceedings of the IEEE International Conference on Intelligent Robots and Systems, Hamburg, Germany."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"104","DOI":"10.1016\/j.cviu.2018.05.001","article-title":"Towards social pattern characterization in egocentric photo-streams","volume":"171","author":"Aghaei","year":"2018","journal-title":"Comput. Vis. Image Underst."},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Swofford, M., Peruzzi, J., Tsoi, N., Thompson, S., Mart\u00edn-Mart\u00edn, R., Savarese, S., and V\u00e1zquez, M. (2020, January 28). Improving Social Awareness Through DANTE: Deep Affinity Network for Clustering Conversational Interactants. Proceedings of the ACM on Human\u2013Computer Interaction, New York, NY, USA.","DOI":"10.1145\/3392824"},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"H\u00fcttenrauch, H., Eklundh, K.S., Green, A., and Topp, E.A. (2006, January 9\u201315). Investigating spatial relationships in human\u2013robot interaction. Proceedings of the 2006 IEEE\/RSJ International Conference on Intelligent Robots and Systems, Beijing, China.","DOI":"10.1109\/IROS.2006.282535"},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Yamaoka, F., Kanda, T., Ishiguro, H., and Hagita, N. (2008, January 12\u201315). How close? Model of proximity control for information-presenting robots. Proceedings of the 2008 3rd ACM\/IEEE International Conference on Human\u2013Robot Interaction (HRI), Amsterdam, The Netherlands.","DOI":"10.1145\/1349822.1349841"},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Kuzuoka, H., Suzuki, Y., Yamashita, J., and Yamazaki, K. (2010, January 2\u20135). Reconfiguring spatial formation arrangement by robot body orientation. Proceedings of the 2010 5th ACM\/IEEE International Conference on Human\u2013Robot Interaction (HRI), Osaka, Japan.","DOI":"10.1109\/HRI.2010.5453182"},{"key":"ref_64","unstructured":"Vroon, J., Joosse, M., Lohse, M., Kolkmeier, J., Kim, J., Truong, K., Englebienne, G., Heylen, D., and Evers, V. (September, January 31). Dynamics of social positioning patterns in group-robot interactions. Proceedings of the IEEE International Workshop on Robot and Human Interactive Communication, Kobe, Japan."},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Johal, W., Jacq, A., Paiva, A., and Dillenbourg, P. (2016, January 26\u201331). Child-robot spatial arrangement in a learning by teaching activity. Proceedings of the 25th IEEE International Symposium on Robot and Human Interactive Communication, RO-MAN 2016, New York, NY, USA.","DOI":"10.1109\/ROMAN.2016.7745169"},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1007\/s12369-012-0166-7","article-title":"Measuring the Quality of Interaction in Mobile Robotic Telepresence: A Pilot\u2019s Perspective","volume":"5","author":"Kristoffersson","year":"2013","journal-title":"Int. J. Soc. Robot."},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"Fangkai, Y., and Christopher Peters, C. (2019, January 14\u201318). AppGAN: Generative adversarial networks for generating robot approach behaviors into small groups of people. Proceedings of the 2019 28th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN), New Delhi, India.","DOI":"10.1109\/RO-MAN46459.2019.8956425"},{"key":"ref_68","doi-asserted-by":"crossref","unstructured":"Gao, Y., Yang, F., Frisk, M., Hemandez, D., Peters, C., and Castellano, G. (2019, January 14\u201318). Learning socially appropriate robot approaching behavior toward groups using deep reinforcement learning. Proceedings of the 2019 28th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN), New Delhi, India.","DOI":"10.1109\/RO-MAN46459.2019.8956444"},{"key":"ref_69","doi-asserted-by":"crossref","unstructured":"Cao, Z., Simon, T., Wei, S.E., and Sheikh, Y. (2016, January 21\u201326). Realtime multi-person 2D pose estimation using part affinity fields. Proceedings of the 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.143"},{"key":"ref_70","unstructured":"Narasimhan, K.P., and White, G. (2013, January 22\u201324). An agent-based analyses of f-formations. Proceedings of the International Conference on Practical Applications of Agents and Multi-Agent Systems, Salamanca, Spain."},{"key":"ref_71","unstructured":"(2021, November 26). Support Vector Machines. Available online: https:\/\/www.datacamp.com\/community\/tutorials\/svm-classification-scikit-learn-python."},{"key":"ref_72","unstructured":"(2021, November 26). Polynomial Kernel. Available online: https:\/\/en.wikipedia.org\/wiki\/Polynomial_kernel."},{"key":"ref_73","unstructured":"(2021, December 23). Support Vector Machines Kernels. Available online: https:\/\/scikit-learn.org\/stable\/modules\/svm.html#svm-kernels."},{"key":"ref_74","doi-asserted-by":"crossref","unstructured":"Zen, G., Lepri, B., Ricci, E., and Lanz, O. (2010, January 29). Space speaks: Towards socially and personality aware visual surveillance. Proceedings of the 1st ACM International Workshop on Multimodal Pervasive Video Analysis, Firenze, Italy.","DOI":"10.1145\/1878039.1878048"},{"key":"ref_75","first-page":"1707","article-title":"Salsa: A novel dataset for multimodal group behavior analysis","volume":"38","author":"Staiano","year":"2015","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_76","doi-asserted-by":"crossref","unstructured":"Pathi, S.K., Kristoffersson, A., Kiselev, A., and Loutfi, A. (2019). F-Formations for Social Interaction in Simulation Using Virtual Agents and Mobile Robotic Telepresence Systems. Multimodal Technol. Interact., 3.","DOI":"10.3390\/mti3040069"},{"key":"ref_77","unstructured":"(2021, October 10). Unity Real-Time Development Platform | 3D, 2D VR & AR Engine. Available online: https:\/\/unity.com\/."},{"key":"ref_78","unstructured":"(2021, April 06). Make Human Community. Available online: http:\/\/makehumancommunity.org\/."},{"key":"ref_79","unstructured":"(2021, June 15). Mixamo. Available online: https:\/\/www.mixamo.com\/."},{"key":"ref_80","unstructured":"(2020, November 26). GitHub-DeNA\/Chainer_Realtime_Multi-Person_Pose_Estimation: Chainer version of Realtime Multi-Person Pose Estiamtion. Available online: https:\/\/github.com\/DeNA\/Chainer_Realtime_Multi-Person_Pose_Estimation."}],"container-title":["Multimodal Technologies and Interaction"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2414-4088\/6\/3\/18\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T22:26:27Z","timestamp":1760135187000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2414-4088\/6\/3\/18"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,2,23]]},"references-count":80,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2022,3]]}},"alternative-id":["mti6030018"],"URL":"https:\/\/doi.org\/10.3390\/mti6030018","relation":{},"ISSN":["2414-4088"],"issn-type":[{"value":"2414-4088","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,2,23]]}}}