Automatic analysis of affective postures and body motion to detect engagement with a game companion,

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It's all in the game: Towards an affect sensitive and context aware game companion

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Rhythmic attention in child-robot dance play

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Engagement in HCI

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Evaluating the Engagement with Social Robots

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Communicating Robot Motion Intent with Augmented Reality

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Human-Robot Interaction: An Introduction

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User profiling and behavioral adaptation for HRI: A survey

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Engagement in human-agent interaction: An overview

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A survey on the design and evolution of social robots — Past, present and future

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A Hybrid Posture Detection Framework: Integrating Machine Learning and Deep Neural Networks

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A Survey of Autonomous Human Affect Detection Methods for Social Robots Engaged in Natural HRI

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The NoXi database: multimodal recordings of mediated novice-expert interactions

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Fully Automatic Analysis of Engagement and Its Relationship to Personality in Human-Robot Interactions

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Towards Engagement Models that Consider Individual Factors in HRI: On the Relation of Extroversion and Negative Attitude Towards Robots to Gaze and Speech During a Human–Robot Assembly Task

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All Work and No Play?

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Detecting and Addressing Frustration in a Serious Game for Military Training

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Modelling empathic behaviour in a robotic game companion for children

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Tracking continuous emotional trends of participants during affective dyadic interactions using body language and speech information

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Fusing Visual and Behavioral Cues for Modeling User Experience in Games

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Comparing Models of Disengagement in Individual and Group Interactions

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Modelling Empathy in Social Robotic Companions

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Fusing Body Posture With Facial Expressions for Joint Recognition of Affect in Child–Robot Interaction

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Bayesian Active Learning-Based Robot Tutor for Children's Word-Reading Skills

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Automatic Detection of Mind Wandering from Video in the Lab and in the Classroom

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Affective State Level Recognition in Naturalistic Facial and Vocal Expressions

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From real-time attention assessment to “with-me-ness” in human-robot interaction

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Making New “New AI” Friends: Designing a Social Robot for Diabetic Children from an Embodied AI Perspective

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Starting engagement detection towards a companion robot using multimodal features

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"Are You Sad, Cozmo?": How Humans Make Sense of a Home Robot's Emotion Displays

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MULTIMODAL AFFECT MODELING AND RECOGNITION FOR EMPATHIC ROBOT COMPANIONS

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Engagement Modeling in Dyadic Interaction

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ENGAGE-DEM: A Model of Engagement of People With Dementia

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Personalized Estimation of Engagement From Videos Using Active Learning With Deep Reinforcement Learning

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Embodied Affect in Tutorial Dialogue: Student Gesture and Posture

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Affective Gaming: A Comprehensive Survey

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A multi-level context-based modeling of engagement in Human-Robot Interaction

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Adaptive Real-Time Emotion Recognition from Body Movements

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Expressive Copying Behavior for Social Agents: A Perceptual Analysis

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From 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 · see citing works

What if Social Robots Look for Productive Engagement?

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Proactive Robots With the Perception of Nonverbal Human Behavior: A Review

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Motion Tracker: Camera-Based Monitoring of Bodily Movements Using Motion Silhouettes

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Learning Deep Spatiotemporal Feature for Engagement Recognition of Online Courses

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Mediating Human-Robot Interactions with Virtual, Augmented, and Mixed Reality

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From the Definition to the Automatic Assessment of Engagement in Human–Robot Interaction: A Systematic Review

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Immersive machine learning for social attitude detection in virtual reality narrative games

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Engagement in longitudinal child-robot language learning interactions: Disentangling robot and task engagement

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Context-Sensitive Affect Recognition for a Robotic Game Companion

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Toward Personalized Affect-Aware Socially Assistive Robot Tutors for Long-Term Interventions with Children with Autism

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Analyzing Posture and Affect in Task-Oriented Tutoring

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Classifying student dialogue acts with multimodal learning analytics

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Automatic Context-Aware Inference of Engagement in HMI: A Survey

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Modeling Feedback in Interaction With Conversational Agents—A Review

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Improving Affect Detection in Game-Based Learning with Multimodal Data Fusion

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Multimodal Affect Recognition: Current Approaches and Challenges

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Configuring Humans: What Roles Humans Play in HRI Research

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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 · see citing works

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 · see citing works

Moving as a Leader

Beyan, C., Katsageorgiou, V., & Murino, V. (2017) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3123266.3123404 · Cited by 30 · see citing works

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 · see citing works

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 · see citing works

The Effect of Emotions and Social Behavior on Performance in a Collaborative Serious Game Between Humans and Autonomous Robots

Jerčić, P., Wen, W., Hagelbäck, J., & Sundstedt, V. (2017) · International Journal of Social Robotics · doi:10.1007/s12369-017-0437-4 · Cited by 29 · see citing works

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 · see citing works

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 28 · see citing works

Development of engagement evaluation method and learning mechanism in an engagement enhancing rehabilitation system

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 · see citing works

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 · see citing works

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 · see citing works

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 25 · see citing works

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 · see citing works

Non-Instrumental Movement Inhibition (NIMI) Differentially Suppresses Head and Thigh Movements during Screenic Engagement: Dependence on Interaction

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 · see citing works

Affective Computational Model to Extract Natural Affective States of Students With Asperger Syndrome (AS) in Computer-Based Learning Environment

Dawood, A. A., Turner, S., & Perepa, P. (2018) · IEEE Access · doi:10.1109/access.2018.2879619 · Cited by 23 · see citing works

Implementation and Validation of Engagement Monitoring in an Engagement Enhancing Rehabilitation System

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 · see citing works

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 · see citing works

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 22 · see citing works

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 22 · see citing works

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 · see citing works

KAPEAN: Understanding Affective States of Children with ADHD.

Martínez, F., Barraza, C., González, N., & Jiménez, J. E. (2016) · Educational Technology & Society · Cited by 22 · see citing works

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 · see citing works

Engagement and Mind Perception Within Human-Robot Interaction: A Comparison Between Elderly and Young Adults

Kont, M., & Alimardani, M. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-62056-1_29 · Cited by 22 · see citing works

Combining Verbal and Nonverbal Features to Overcome the ``Information Gap'' in Task-Oriented Dialogue

Ha, E. Y., Grafsgaard, J. F., Mitchell, C. M., Boyer, K. E., & Lester, J. C. (2012) · Cited by 21 · see citing works

Classifying a Person’s Degree of Accessibility From Natural Body Language During Social Human–Robot Interactions

McColl, D., Jiang, C., & Nejat, G. (2016) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2016.2520367 · Cited by 21 · see citing works

Sit still and pay attention: Using the Wii Balance-Board to detect lapses in concentration in children during psychophysical testing

Jones, P. R. (2018) · Behavior Research Methods · doi:10.3758/s13428-018-1045-4 · Cited by 21 · see citing works

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 20 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

Towards an affect sensitive interactive companion

Soyel, H., & McOwan, P. W. (2013) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2013.03.010 · Cited by 10 · see citing works

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 36 · see citing works

Designing a game companion for long-term social interaction

Leite, I., Castellano, G., Pereira, A., Martinho, C., Paiva, A., & McOwan, P. W. (2009) · doi:10.1145/1655260.1655270 · Cited by 9 · see citing works

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 · see citing works

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 · see citing works