TY - GEN
T1 - Estimating User Engagement in Human Robot Interaction Using a Dynamic Bayesian Network
AU - Hei, Xiaoxuan
AU - Zhang, Heng
AU - Tapus, Adriana
N1 - Publisher Copyright:
© 2025 IEEE.
PY - 2025/1/1
Y1 - 2025/1/1
N2 - Engagement is a key concept in Human-Robot Interaction (HRI), as high engagement often leads to improved user experience and task performance. However, accurately estimating engagement during interactions is challenging. In this study, we propose a Dynamic Bayesian Network (DBN) to infer user engagement from various modalities, including head rotation, eye movements, facial expressions captured through visual sensors, as well as facial temperature variations measured by a thermal camera. Data was gathered from a human-robot interaction (HRI) experiment, where a robot guided participants and encouraged them to share their thoughts and insights on environmental issues. Our approach successfully combines these diverse features to offer a thorough assessment of user engagement. The network was tested on its capacity to classify participants as either engaged or not engaged, achieving an accuracy of 0.83 and an Area Under the Curve (AUC) of 0.82. These findings underscore the strength of our DBN in detecting user engagement during interactions.
AB - Engagement is a key concept in Human-Robot Interaction (HRI), as high engagement often leads to improved user experience and task performance. However, accurately estimating engagement during interactions is challenging. In this study, we propose a Dynamic Bayesian Network (DBN) to infer user engagement from various modalities, including head rotation, eye movements, facial expressions captured through visual sensors, as well as facial temperature variations measured by a thermal camera. Data was gathered from a human-robot interaction (HRI) experiment, where a robot guided participants and encouraged them to share their thoughts and insights on environmental issues. Our approach successfully combines these diverse features to offer a thorough assessment of user engagement. The network was tested on its capacity to classify participants as either engaged or not engaged, achieving an accuracy of 0.83 and an Area Under the Curve (AUC) of 0.82. These findings underscore the strength of our DBN in detecting user engagement during interactions.
UR - https://www.scopus.com/pages/publications/105016703551
U2 - 10.1109/ICRA55743.2025.11128383
DO - 10.1109/ICRA55743.2025.11128383
M3 - Conference contribution
AN - SCOPUS:105016703551
T3 - Proceedings - IEEE International Conference on Robotics and Automation
SP - 11242
EP - 11248
BT - 2025 IEEE International Conference on Robotics and Automation, ICRA 2025
A2 - Ott, Christian
A2 - Admoni, Henny
A2 - Behnke, Sven
A2 - Bogdan, Stjepan
A2 - Bolopion, Aude
A2 - Choi, Youngjin
A2 - Ficuciello, Fanny
A2 - Gans, Nicholas
A2 - Gosselin, Clement
A2 - Harada, Kensuke
A2 - Kayacan, Erdal
A2 - Kim, H. Jin
A2 - Leutenegger, Stefan
A2 - Liu, Zhe
A2 - Maiolino, Perla
A2 - Marques, Lino
A2 - Matsubara, Takamitsu
A2 - Mavromatti, Anastasia
A2 - Minor, Mark
A2 - O'Kane, Jason
A2 - Park, Hae Won
A2 - Park, Hae-Won
A2 - Rekleitis, Ioannis
A2 - Renda, Federico
A2 - Ricci, Elisa
A2 - Riek, Laurel D.
A2 - Sabattini, Lorenzo
A2 - Shen, Shaojie
A2 - Sun, Yu
A2 - Wieber, Pierre-Brice
A2 - Yamane, Katsu
A2 - Yu, Jingjin
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2025 IEEE International Conference on Robotics and Automation, ICRA 2025
Y2 - 19 May 2025 through 23 May 2025
ER -