The DOI matches its Crossref record; 32 of the 34 references it cites were found in a registry or an index.
Automatic analysis of affective postures and body motion to detect engagement with a game companion,
Abstract
The design of an affect recognition system for socially perceptive robots relies on representative data: human-robot interaction in naturalistic settings requires an affect recognition system to be trained and validated with contextualised affective expressions, that is, expressions that emerge in the same interaction scenario of the target application. In this paper we propose an initial computational model to automatically analyse human postures and body motion to detect engagement of children playing chess with an iCat robot that acts as a game companion. Our approach is based on vision-based automatic extraction of expressive postural features from videos capturing the behaviour of the children from a lateral view. An initial evaluation, conducted by training several recognition models with contextualised affective postural expressions, suggests that patterns of postural behaviour can be used to accurately predict the engagement of the children with the robot, thus making our approach suitable for integration into an affect recognition system for a game companion in a real world scenario.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“The user's body pose can be used to estimate their intent to engage with an embodied agent (Bohus and Horvitz, 2009; Sanghvi et al., 2011; Schwarz et al., 2014).”
From Modeling Feedback in Interaction With Conversational Agents—A Review (2022) · cites this paper as Sanghvi et al., 2011
Citations
Cites
Data Mining: Practical Machine Learning Tools and Techniques
Witten, I. H., Frank, E., & Hall, M. A. (2011) · Elsevier eBooks · doi:10.1016/c2009-0-19715-5 · Cited by 25,537
Witten, I. H. (2008) · Cited by 9,143
A Survey of Affect Recognition Methods: Audio, Visual, and Spontaneous Expressions
Zeng, Z., Pantic, M., Roisman, G. I., & Huang, T. S. (2009) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2008.52 · Cited by 2,797
Socially intelligent robots: Dimensions of human-robot interaction
Dautenhahn, K. (2007) · Philosophical Transactions of the Royal Society B: Biological Sciences · doi:10.1098/rstb.2006.2004 · Cited by 1,210 · cited 1×
“Robots that are to be used in applications that require direct interaction with human users, for example robot companions, have been shown to benefit from the integration of socially intelligent behaviour [6] [13].”
BackgroundAutomatic prediction of frustration
Kapoor, A., Burleson, W., & Picard, R. W. (2007) · International Journal of Human-Computer Studies · doi:10.1016/j.ijhcs.2007.02.003 · Cited by 592
Socialization between toddlers and robots at an early childhood education center
Tanaka, F., Cicourel, A. V., & Movellan, J. R. (2007) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0707769104 · Cited by 470
Camurri, A., Lagerlöf, I., & Volpe, G. (2003) · International Journal of Human-Computer Studies · doi:10.1016/s1071-5819(03)00050-8 · Cited by 412
Recognising Human Emotions from Body Movement and Gesture Dynamics
Castellano, G., Villalba, S. D., & Camurri, A. (2007) · Lecture notes in computer science · doi:10.1007/978-3-540-74889-2_7 · Cited by 335
Self-citationCues and channels in emotion recognition.
Wallbott, H. G., & Scherer, K. R. (1986) · Journal of Personality and Social Psychology · doi:10.1037/0022-3514.51.4.690 · Cited by 326
Automated Posture Analysis for Detecting Learner's Interest Level
Mota, S., & Picard, R. W. (2003) · IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2003.10047 · Cited by 315
Recognizing engagement in human-robot interaction
Rich, C., Ponsler, B., Holroyd, A., & Sidner, C. L. (2010) · ACM/IEEE International Conference on Human-Robot Interaction (HRI) · doi:10.1109/hri.2010.5453163 · Cited by 266
Affective state estimation for human–robot Interaction
Kulic, D., & Croft, E. A. (2007) · IEEE Transactions on Robotics · doi:10.1109/tro.2007.904899 · Cited by 230
Automatic Temporal Segment Detection and Affect Recognition From Face and Body Display
Gunes, H., & Piccardi, M. (2009) · IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics) · doi:10.1109/tsmcb.2008.927269 · Cited by 225
Children's decoding of emotion in expressive body movement: The development of cue attunement.
Boone, R. T., & Cunningham, J. G. (1998) · Developmental Psychology · doi:10.1037//0012-1649.34.5.1007 · Cited by 225
Mind, hands, face and body. A goal and belief view of multimodal communication
Poggi, I. (2007) · Cited by 209
van Breemen, A., Xue, Y., & Meerbeek, B. (2005) · Adaptive Agents and Multi-Agents Systems · doi:10.1145/1082473.1082823 · Cited by 204
Detecting user engagement with a robot companion using task and social interaction-based features
Castellano, G., Pereira, A., Leite, I., Paiva, A., & McOwan, P. W. (2009) · INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION · doi:10.1145/1647314.1647336 · Cited by 172
Self-citationEstimating user's engagement from eye-gaze behaviors in human-agent conversations
Nakano, Y., & Ishii, R. (2010) · ACM International Conference on Intelligent User Interfaces (IUI) · doi:10.1145/1719970.1719990 · Cited by 159
AUTOMATIC DETECTION OF LEARNER'S AFFECT FROM GROSS BODY LANGUAGE
D’Mello, S. K., & Graesser, A. C. (2009) · Applied Artificial Intelligence · doi:10.1080/08839510802631745 · Cited by 143
Detecting Affect from Non-stylised Body Motions
Bernhardt, D., & Robinson, P. (2007) · Lecture notes in computer science · doi:10.1007/978-3-540-74889-2_6 · Cited by 141
Role of expressive behaviour for robots that learn from people
Breazeal, C. (2009) · Philosophical Transactions of the Royal Society B Biological Sciences · doi:10.1098/rstb.2009.0157 · Cited by 133
Are emotional robots more fun to play with?
Leite, I., Pereira, A., Martinho, C., & Paiva, A. (2008) · IEEE RO-MAN · doi:10.1109/roman.2008.4600646 · Cited by 125
Self-citationBetsch, T., Fiedler, K., & Brinkmann, J. (1998) · European Journal of Social Psychology · doi:10.1002/(sici)1099-0992(1998110)28:6 · Cited by 110
A categorical approach to affective gesture recognition
Bianchi‐Berthouze, N., & Kleinsmith, A. (2003) · Connection Science · doi:10.1080/09540090310001658793 · Cited by 93
Affect recognition for interactive companions: challenges and design in real world scenarios
Castellano, G., Leite, I., Pereira, A., Martinho, C., Paiva, A., & McOwan, P. W. (2009) · Journal on Multimodal User Interfaces · doi:10.1007/s12193-009-0033-5 · Cited by 86
Self-citationSpatiotemporal Coding in ANVIL
Kipp, M. (2008) · Language Resources and Evaluation Conference (LREC) · doi:10.63317/25wozp3pdwif · Cited by 72
Investigating shared attention with a virtual agent using a gaze-based interface
Peters, C., Asteriadis, S., & Karpouzis, K. C. (2009) · Journal on Multimodal User Interfaces · doi:10.1007/s12193-009-0029-1 · Cited by 51
Behaviour delay and robot expressiveness in child-robot interactions
Robins, B., Dautenhahn, K., Boekhorst, R. T., & Nehaniv, C. L. (2008) · ACM/IEEE International Conference on Human-Robot Interaction (HRI) · doi:10.1145/1349822.1349826 · Cited by 49
It's all in the game: Towards an affect sensitive and context aware game companion
Castellano, G., Leite, I., Pereira, A., Martinho, C., Paiva, A., & McOwan, P. W. (2009) · International Conference on Affective Computing and Intelligent Interaction and workshops · doi:10.1109/acii.2009.5349558 · Cited by 41
Self-citationRhythmic attention in child-robot dance play
Michalowski, M. P., Simmons, R. G., & Kozima, H. (2009) · IEEE RO-MAN · doi:10.1109/roman.2009.5326143 · Cited by 35
Recognizing engagement in human-robot interaction
Rich, C., Ponsleur, B., Holroyd, A., & Sidner, C. L. (2010) · doi:10.5555/1734454.1734580 · Cited by 31
Castellano, G., & Peters, C. (2010) · Interaction Studies Social Behaviour and Communication in Biological and Artificial Systems · doi:10.1075/is.11.2.04cas · Cited by 19
Self-citation
Research works printed but not linked: 4
- S. Afzal and P. Robinson . Natural affect data - collection and annotation in learning context . In Proceedings of the 3rd International Conference on Affective Computing and Intelligent Interaction (ACII 2009 ), pages 22 -- 28 . IEEE, 2009 . S. Afzal and P. Robinson. Natural affect data - collection and annotation in learning context. In Proceedings of the 3rd International Conference on Affective Computing and Intelligent Interaction (ACII 2009), pages 22--28. IEEE, 2009.
- 10.1037/0012-1649.34.5.1007 · DOI
- G. Bradski and A. Kaehler . Learning OpenCV: Computer Vision with the OpenCV Library. O'Reilly , 2008 . G. Bradski and A. Kaehler. Learning OpenCV: Computer Vision with the OpenCV Library. O'Reilly, 2008.
- 10.5555/1630659.1630684 · DOI
Cited by
Sorted by citations
Affective Body Expression Perception and Recognition: A Survey
Kleinsmith, A., & Bianchi‐Berthouze, N. (2012) · IEEE Transactions on Affective Computing · doi:10.1109/t-affc.2012.16 · Cited by 624
Gunes, H., & Schuller, B. (2013) · Image and Vision Computing · doi:10.1016/j.imavis.2012.06.016 · Cited by 360
Personalized machine learning for robot perception of affect and engagement in autism therapy
Rudovic, O., Lee, J., Dai, M., Schuller, B., & Picard, R. W. (2018) · Science Robotics · doi:10.1126/scirobotics.aao6760 · Cited by 316
Barua, P. D., Vicnesh, J., Gururajan, R., Oh, S. L., Palmer, E., Azizan, M. M., Kadri, N. A., & Acharya, U. R. (2022) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph19031192 · Cited by 270
Doherty, K., & Doherty, G. J. (2018) · ACM Computing Surveys · doi:10.1145/3234149 · Cited by 299
Metadata
- Authors
- Sanghvi, J., Castellano, G., Leite, I., Pereira, A., McOwan, P. W., & Paiva, A.
- Affiliations
- Queen Mary University of London (GB)
- Instituto Superior Técnico (PT)
- Venue
- Proceedings of the 6th international conference on Human-robot interaction
- Publisher
- ACM
- Published
- 2011-03-06
- Pages
- 305-312
- OpenAlex
- W2105410877
- Cited by
- 250
- References
- 34
- Citations by year
- 2026: 2 · 2025: 9 · 2024: 15 · 2023: 9 · 2022: 25 · 2021: 14 · 2020: 16 · 2019: 26 · 2018: 20 · 2017: 16 · 2016: 26 · 2015: 20 · 2014: 15 · 2013: 20 · 2012: 15
- FWCI
- 26.44
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- ACM/IEEE International Conference on Human-Robot Interaction (HRI) (publishedVersion)
- Funders
- Canadian Institute for Theoretical Astrophysics
- FP7 Information and Communication Technologies
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Emotion and Mood Recognition
- Social Sciences › Psychology › Social Psychology › Social Robot Interaction and HRI
- Social Sciences › Psychology › Social Psychology › Health, Education, and Physical Culture
- Keywords
- affect recognition, human-robot interaction, body motion analysis, chess
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
