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
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
Evaluating the Engagement with Social Robots
Anzalone, S. M., Boucenna, S., Ivaldi, S., & Chétouani, M. (2015) · International Journal of Social Robotics · doi:10.1007/s12369-015-0298-7 · Cited by 236
Communicating Robot Motion Intent with Augmented Reality
Walker, M., Hedayati, H., Lee, J., & Szafir, D. J. (2018) · ACM/IEEE International Conference on Human-Robot Interaction (HRI) · doi:10.1145/3171221.3171253 · Cited by 232
Human-Robot Interaction: An Introduction
Bartneck, C., Belpaeme, T., Eyssel, F., Kanda, T., Keijsers, M., & Šabanović, S. (2020) · doi:10.1017/9781108676649 · Cited by 211
User profiling and behavioral adaptation for HRI: A survey
Rossi, S., Ferland, F., & Tapus, A. (2017) · Pattern Recognition Letters · doi:10.1016/j.patrec.2017.06.002 · Cited by 188
Engagement in human-agent interaction: An overview
Oertel, C., Castellano, G., Chetouani, M., Nasir, J., Obaid, M., Pelachaud, C., & Peters, C. (2020) · Frontiers in Robotics and AI · doi:10.3389/frobt.2020.00092 · Cited by 160
Self-citationA survey on the design and evolution of social robots — Past, present and future
Mahdi, H., Akgun, S. A., Saleh, S., & Dautenhahn, K. (2022) · Robotics and Autonomous Systems · doi:10.1016/j.robot.2022.104193 · Cited by 181
A Hybrid Posture Detection Framework: Integrating Machine Learning and Deep Neural Networks
Liaqat, S., Dashtipour, K. K., Arshad, K., Assaleh, K. T., & Ramzan, N. (2021) · IEEE Sensors Journal · doi:10.1109/jsen.2021.3055898 · Cited by 148
A Survey of Autonomous Human Affect Detection Methods for Social Robots Engaged in Natural HRI
McColl, D., Hong, A., Hatakeyama, N., Nejat, G., & Benhabib, B. (2015) · Journal of Intelligent & Robotic Systems · doi:10.1007/s10846-015-0259-2 · Cited by 127
The NoXi database: multimodal recordings of mediated novice-expert interactions
Cafaro, A., Wagner, J., Baur, T., Dermouche, S., Torres, M. T., Pélachaud, C., André, E., & Valstar, M. (2017) · INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION · doi:10.1145/3136755.3136780 · Cited by 125
Salam, H., Çeliktutan, O., Hupont, I., Güneş, H., & Chétouani, M. (2016) · IEEE Access · doi:10.1109/access.2016.2614525 · Cited by 116
Ivaldi, S., Lefort, S., Peters, J., Chétouani, M., Provasi, J., & Zibetti, E. (2016) · International Journal of Social Robotics · doi:10.1007/s12369-016-0357-8 · Cited by 114
Liao, Q. V., Mas-ud Hussain, M., Chandar, P., Davis, M., Khazaeni, Y., Crasso, M. P., Wang, D., Muller, M., Shami, N. S., & Geyer, W. (2018) · Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3173574.3173577 · Cited by 116
Detecting and Addressing Frustration in a Serious Game for Military Training
DeFalco, J. A., Rowe, J. P., Paquette, L., Georgoulas-Sherry, V., Brawner, K. W., Mott, B. W., Baker, R., & Lester, J. C. (2017) · International Journal of Artificial Intelligence in Education · doi:10.1007/s40593-017-0152-1 · Cited by 106
Modelling empathic behaviour in a robotic game companion for children
Leite, I., Castellano, G., Pereira, A., Martinho, C., & Paiva, A. (2012) · ACM/IEEE International Conference on Human-Robot Interaction (HRI) · doi:10.1145/2157689.2157811 · Cited by 108
Self-citationMetallinou, A., Katsamanis, A., & Narayanan, S. S. (2012) · Image and Vision Computing · doi:10.1016/j.imavis.2012.08.018 · Cited by 105
Fusing Visual and Behavioral Cues for Modeling User Experience in Games
Shaker, N., Asteriadis, S., Yannakakis, G. N., & Karpouzis, K. C. (2013) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2013.2271738 · Cited by 91
Comparing Models of Disengagement in Individual and Group Interactions
Leite, I., McCoy, M., Ullman, D. L., Salomons, N., & Scassellati, B. M. (2015) · ACM/IEEE International Conference on Human-Robot Interaction (HRI) · doi:10.1145/2696454.2696466 · Cited by 91
Self-citationModelling Empathy in Social Robotic Companions
Leite, I., Pereira, A., Castellano, G., Mascarenhas, S. F., Martinho, C., & Paiva, A. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-28509-7_14 · Cited by 90
Self-citationFilntisis, P. P., Efthymiou, N., Koutras, P., Potamianos, G., & Maragos, P. A. (2019) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2019.2930434 · Cited by 90
Bayesian Active Learning-Based Robot Tutor for Children's Word-Reading Skills
Gordon, G., & Breazeal, C. (2015) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v29i1.9376 · Cited by 75
Automatic Detection of Mind Wandering from Video in the Lab and in the Classroom
Bosch, N., & D’Mello, S. K. (2019) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2019.2908837 · Cited by 80
Affective State Level Recognition in Naturalistic Facial and Vocal Expressions
Meng, H., & Bianchi‐Berthouze, N. (2013) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2013.2253768 · Cited by 75
Definitions of engagement in human-agent interaction
Glas, N., & Pélachaud, C. (2015) · International Conference on Affective Computing and Intelligent Interaction and workshops · doi:10.1109/acii.2015.7344688 · Cited by 73
From real-time attention assessment to “with-me-ness” in human-robot interaction
Lemaignan, S., García, F., Jacq, A., & Dillenbourg, P. (2016) · ACM/IEEE International Conference on Human-Robot Interaction (HRI) · doi:10.1109/hri.2016.7451747 · Cited by 70
Cañamero, L. D., & Lewis, M. (2016) · International Journal of Social Robotics · doi:10.1007/s12369-016-0364-9 · Cited by 64
Starting engagement detection towards a companion robot using multimodal features
Vaufreydaz, D., Johal, W., & Combe, C. (2015) · Robotics and Autonomous Systems · doi:10.1016/j.robot.2015.01.004 · Cited by 63
Computational Human-Robot Interaction
Thomaz, A. L., Hoffman, G., & Çakmak, M. (2016) · now publishers, Inc. eBooks · doi:10.1561/9781680832099 · Cited by 63
Bodily Expression for Automatic Affect Recognition
Gunes, H., Shan, C., Chen, S., & Tian, Y.-L. (2015) · doi:10.1002/9781118910566.ch14 · Cited by 58
"Are You Sad, Cozmo?": How Humans Make Sense of a Home Robot's Emotion Displays
Pelikan, H. R. M., Broth, M., & Keevallik, L. (2020) · Proceedings of the 2020 ACM/IEEE International Conference on Human-Robot Interaction · doi:10.1145/3319502.3374814 · Cited by 64
Karimah, S. N., & Hasegawa, S. (2022) · Smart Learning Environments · doi:10.1186/s40561-022-00212-y · Cited by 76
Li, G., Zarei, M. A., Alibakhshi, G., & Labbafi, A. (2024) · BMC Psychology · doi:10.1186/s40359-024-01664-2 · Cited by 78
Detecting Engagement in HRI: An Exploration of Social and Task-Based Context
Castellano, G., Leite, I., Pereira, A., Martinho, C., Paiva, A., & McOwan, P. W. (2012) · International Conference on Social Computing · doi:10.1109/socialcom-passat.2012.51 · Cited by 56
Self-citationAutomatic Recognition of Children Engagement from Facial Video Using Convolutional Neural Networks
Yun, W., Lee, D., Park, C., Kim, J., & Kim, J. (2018) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2018.2834350 · Cited by 56
Predicting Learning and Affect from Multimodal Data Streams in Task-Oriented Tutorial Dialogue.
Grafsgaard, J. F., Wiggins, J. B., Boyer, K. E., Wiebe, E., & Lester, J. C. (2014) · Cited by 52
MULTIMODAL AFFECT MODELING AND RECOGNITION FOR EMPATHIC ROBOT COMPANIONS
CASTELLANO, G., LEITE, I., PEREIRA, A., MARTINHO, C., PAIVA, A., & MCOWAN, P. W. (2013) · International Journal of Humanoid Robotics · doi:10.1142/s0219843613500102 · Cited by 51
Self-citationMulti-modal Active Learning From Human Data: A Deep Reinforcement Learning Approach
Rudovic, O. O., Zhang, M., Schuller, B. W., & Picard, R. W. (2019) · INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION · doi:10.1145/3340555.3353742 · Cited by 52
Multimodal analysis of the implicit affective channel in computer-mediated textual communication
Grafsgaard, J. F., Fulton, R. M., Boyer, K., Wiebe, E., & Lester, J. C. (2012) · International Conference on Multimodal Interaction · doi:10.1145/2388676.2388708 · Cited by 54
Engagement Modeling in Dyadic Interaction
Dermouche, S., & Pélachaud, C. (2019) · INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION · doi:10.1145/3340555.3353765 · Cited by 46
Schwarz, J., Marais, C. C., Leyvand, T., Hudson, S. E., & Mankoff, J. (2014) · Proceedings of the SIGCHI Conference on Human Factors in Computing Systems · doi:10.1145/2556288.2556989 · Cited by 45
ENGAGE-DEM: A Model of Engagement of People With Dementia
Perugia, G., Boladeras, M. D., Catalá, A., Barakova, E. I., & Rauterberg, M. (2020) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2020.2980275 · Cited by 45
Rudovic, O. O., Park, H. W., Busche, J., Schuller, B. W., Breazeal, C., & Picard, R. W. (2019) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2019.00031 · Cited by 43
Embodied Affect in Tutorial Dialogue: Student Gesture and Posture
Grafsgaard, J. F., Wiggins, J. B., Boyer, K., Wiebe, E., & Lester, J. C. (2013) · International Conference on Artificial Intelligence in Education · doi:10.1007/978-3-642-39112-5_1 · Cited by 50
Affective Gaming: A Comprehensive Survey
Kotsia, I., Zafeiriou, S. P., & Fotopoulos, S. (2013) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2013.100 · Cited by 43
A multi-level context-based modeling of engagement in Human-Robot Interaction
Salam, H., & Chétouani, M. (2015) · International Conference on Automatic Face and Gesture Recognition · doi:10.1109/fg.2015.7284845 · Cited by 42
Adaptive Real-Time Emotion Recognition from Body Movements
Wang, W., Enescu, V., & Hichem Sahli, B. (2015) · ACM Transactions on Interactive Intelligent Systems · doi:10.1145/2738221 · Cited by 42
Expressive Copying Behavior for Social Agents: A Perceptual Analysis
Castellano, G., Mancini, M., Peters, C., & McOwan, P. W. (2011) · IEEE Transactions on Systems Man and Cybernetics - Part A Systems and Humans · doi:10.1109/tsmca.2011.2172415 · Cited by 41
Self-citationFrom real-time attention assessment to “with-me-ness” in human-robot interaction
Lemaignan, S., Garcia, F., Jacq, A., & Dillenbourg, P. (2016) · UWE Research Repository (UWE Bristol) · Cited by 41
What if Social Robots Look for Productive Engagement?
Nasir, J., Bruno, B., Chétouani, M., & Dillenbourg, P. (2021) · International Journal of Social Robotics · doi:10.1007/s12369-021-00766-w · Cited by 48
Proactive Robots With the Perception of Nonverbal Human Behavior: A Review
Sirithunge, C., Jayasekara, A. G. B. P., & Chandima, D. P. (2019) · IEEE Access · doi:10.1109/access.2019.2921986 · Cited by 40
Motion Tracker: Camera-Based Monitoring of Bodily Movements Using Motion Silhouettes
Kory-Westlund, J. M., D’Mello, S. K., & Olney, A. M. (2015) · PLoS ONE · doi:10.1371/journal.pone.0130293 · Cited by 40
Learning Deep Spatiotemporal Feature for Engagement Recognition of Online Courses
Geng, L., Xu, M. G., Wei, Z., & Zhou, X. (2019) · IEEE Symposium Series on Computational Intelligence (SSCI) · doi:10.1109/ssci44817.2019.9002713 · Cited by 39
Mediating Human-Robot Interactions with Virtual, Augmented, and Mixed Reality
Szafir, D. J. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-21565-1_9 · Cited by 38
Sorrentino, A., Fiorini, L., & Cavallo, F. (2024) · International Journal of Social Robotics · doi:10.1007/s12369-024-01146-w · Cited by 43
Immersive machine learning for social attitude detection in virtual reality narrative games
Dobre, G. C., Gillies, M. F. P., & Pan, X. (2022) · Virtual Reality · doi:10.1007/s10055-022-00644-4 · Cited by 43
de Haas, M., Vogt, P., van den Berghe, R., Leseman, P. P. M., Oudgenoeg‐Paz, O., Willemsen, B., de Wit, J., & Krahmer, E. (2022) · International Journal of Child-Computer Interaction · doi:10.1016/j.ijcci.2022.100501 · Cited by 42
Context-Sensitive Affect Recognition for a Robotic Game Companion
Castellano, G., Leite, I., Pereira, A., Martinho, C., Paiva, A., & McOwan, P. W. (2014) · ACM Transactions on Interactive Intelligent Systems · doi:10.1145/2622615 · Cited by 37
Self-citationShi, Z., Groechel, T. R., Jain, S., Chima, K., Rudovic, O. O., & Matarić, M. J. (2022) · ACM Transactions on Human-Robot Interaction · doi:10.1145/3526111 · Cited by 43
Analyzing Posture and Affect in Task-Oriented Tutoring
Grafsgaard, J. F., Boyer, K. E., Wiebe, E., & Lester, J. C. (2012) · The Florida AI Research Society · Cited by 36
Classifying student dialogue acts with multimodal learning analytics
Ezen-Can, A., Grafsgaard, J. F., Lester, J. C., & Boyer, K. E. (2015) · International Conference on Learning Analytics and Knowledge (LAK) · doi:10.1145/2723576.2723588 · Cited by 35
Automatic Context-Aware Inference of Engagement in HMI: A Survey
Salam, H., Çeliktutan, O., Güneş, H., & Chétouani, M. (2023) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2023.3278707 · Cited by 50
Modeling Feedback in Interaction With Conversational Agents—A Review
Axelsson, A., Buschmeier, H., & Skantze, G. (2022) · Frontiers in Computer Science · doi:10.3389/fcomp.2022.744574 · Cited by 45
“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).”
BackgroundImproving Affect Detection in Game-Based Learning with Multimodal Data Fusion
Henderson, N., Rowe, J., Paquette, L., Baker, R. S., & Lester, J. C. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-52237-7_19 · Cited by 33
Multimodal Affect Recognition: Current Approaches and Challenges
Osman, H. A., & Falk, T. H. (2017) · InTech eBooks · doi:10.5772/65683 · Cited by 37
Configuring Humans: What Roles Humans Play in HRI Research
Lee, H. R., Cheon, E., Lim, C., & Fischer, K. (2022) · ACM/IEEE International Conference on Human-Robot Interaction (HRI) · doi:10.1109/hri53351.2022.9889496 · Cited by 32
Wizard of Oz vs autonomous: Children's perception changes according to robot's operation condition
Tozadore, D. C., Pinto, A. H. M., Romero, R. A. F., & Trovato, G. (2017) · IEEE RO-MAN · doi:10.1109/roman.2017.8172374 · Cited by 31
Automatic Affect Perception Based on Body Gait and Posture: A Survey
Stephens-Fripp, B., Naghdy, F., Stirling, D., & Naghdy, G. (2017) · International Journal of Social Robotics · doi:10.1007/s12369-017-0427-6 · Cited by 27
Beyan, C., Katsageorgiou, V., & Murino, V. (2017) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3123266.3123404 · Cited by 30
Functional movement screen dataset collected with two Azure Kinect depth sensors
Xing, Q., Shen, Y., Cao, R., Zong, S., Shu-xiang, Z., & Shen, Y. (2022) · Scientific Data · doi:10.1038/s41597-022-01188-7 · Cited by 30
Multimodal learning analytics: assessing learners' mental state during the process of learning
Oviatt, S. L., Grafsgaard, J. F., Chen, L., & Ochôa, X. (2018) · ACM eBooks · doi:10.1145/3107990.3108003 · Cited by 30
Jerčić, P., Wen, W., Hagelbäck, J., & Sundstedt, V. (2017) · International Journal of Social Robotics · doi:10.1007/s12369-017-0437-4 · Cited by 29
Modeling Dynamics of Expressive Body Gestures In Dyadic Interactions
Yang, Z., & Narayanan, S. S. (2016) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2016.2542812 · Cited by 28
Addressing joint action challenges in HRI: Insights from psychology and philosophy
Belhassein, K., Castro, V. F., Mayima, A., Clodic, A., Pacherie, É., Guidetti, M., Alami, R., & Cochet, H. (2021) · Acta Psychologica · doi:10.1016/j.actpsy.2021.103476 · Cited by 35
Li, C., Rusák, Z., Horváth, I., & Ji, L. (2016) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2016.01.021 · Cited by 27
Using body movement and posture for emotion detection in non-acted scenarios
Garber-Barron, M., & Si, M. (2012) · IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) · doi:10.1109/fuzz-ieee.2012.6250780 · Cited by 26
Applications of Automated Facial Coding in Media Measurement
McDuff, D., & el Kaliouby, R. (2016) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2016.2571284 · Cited by 26
MyoTrack: Realtime Estimation of Subject Participation in Robotic Rehabilitation Using sEMG and IMU
Pareek, S., Manjunath, H., Esfahani, E. T., & Kesavadas, T. (2019) · IEEE Access · doi:10.1109/access.2019.2922325 · Cited by 26
User Affect Elicitation with a Socially Emotional Robot
Shao, M., Snyder, M., Nejat, G., & Benhabib, B. (2020) · Robotics · doi:10.3390/robotics9020044 · Cited by 25
Witchel, H. J., Santos, C. P., Ackah, J. K., Westling, C. E. I., & Chockalingam, N. (2016) · Frontiers in Psychology · doi:10.3389/fpsyg.2016.00157 · Cited by 24
Dawood, A. A., Turner, S., & Perepa, P. (2018) · IEEE Access · doi:10.1109/access.2018.2879619 · Cited by 25
Li, C., Rusák, Z., Horváth, I., Kooijman, A. C., & Ji, L. (2016) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2016.2591183 · Cited by 22
The Disappearing Robot: An Analysis of Disengagement and Distraction During Non-Interactive Tasks
Rossi, S., Ercolano, G., Raggioli, L., Savino, E., & Ruocco, M. (2018) · IEEE RO-MAN · doi:10.1109/roman.2018.8525514 · Cited by 22
User Profiling to Enhance Clinical Assessment and Human–Robot Interaction: A Feasibility Study
Fiorini, L., Coviello, L., Sorrentino, A., Sancarlo, D., Ciccone, F., D’Onofrio, G., Mancioppi, G., Rovini, E., & Cavallo, F. (2022) · International Journal of Social Robotics · doi:10.1007/s12369-022-00901-1 · Cited by 23
Long-Term Exercise Assistance: Group and One-on-One Interactions between a Social Robot and Seniors
Shao, M., Pham-Hung, M., Alves, S. F. D. R., Snyder, M., Eshaghi, K., Benhabib, B., & Nejat, G. (2023) · Robotics · doi:10.3390/robotics12010009 · Cited by 24
MultiMediate '23: Engagement Estimation and Bodily Behaviour Recognition in Social Interactions
Müller, P., Balážia, M., Baur, T., Dietz, M., Heimerl, A., Schiller, D., Guermal, M., Thomas, D., Brémond, F., Alexandersson, J., et al. (2023) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3581783.3613851 · Cited by 22
KAPEAN: Understanding Affective States of Children with ADHD.
Martínez, F., Barraza, C., González, N., & Jiménez, J. E. (2016) · Educational Technology & Society · Cited by 22
Joint Emotional State of Children and Perceived Collaborative Experience in Coding Activities
Sharma, K., Papavlasopoulou, S., & Giannakos, M. N. (2019) · ACM International Conference on Interaction Design and Children (IDC) · doi:10.1145/3311927.3323145 · Cited by 23
Kont, M., & Alimardani, M. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-62056-1_29 · Cited by 22
Ha, E. Y., Grafsgaard, J. F., Mitchell, C. M., Boyer, K. E., & Lester, J. C. (2012) · Cited by 21
McColl, D., Jiang, C., & Nejat, G. (2016) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2016.2520367 · Cited by 21
Jones, P. R. (2018) · Behavior Research Methods · doi:10.3758/s13428-018-1045-4 · Cited by 23
Analysis of engagement behavior in children during dyadic interactions using prosodic cues
Gupta, R., Bone, D., Lee, S., & Narayanan, S. S. (2015) · Computer Speech & Language · doi:10.1016/j.csl.2015.09.003 · Cited by 21
A Re-Analysis and Synthesis of Data on Affect Dynamics in Learning
Karumbaiah, S., Baker, R. S., Ocumpaugh, J. L., & Andres, J. M. A. L. (2021) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2021.3086118 · Cited by 20
Determining the affective body language of older adults during socially assistive HRI
McColl, D., & Nejat, G. (2014) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2014.6942922 · Cited by 20
Social Robots and Virtual Humans as Assistive Tools for Improving Our Quality of Life
Magnenat‐Thalmann, N., & Zhang, Z. (2014) · International Conference on Digital Home (ICDH) · doi:10.1109/icdh.2014.8 · Cited by 20
Towards player’s affective and behavioral visual cues as drives to game adaptation
Asteriadis, S., Shaker, N., Karpouzis, K. C., & Yannakakis, G. N. (2012) · OAR@UM (University of Malta) · Cited by 19
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
