Emotion Recognition for Human-Robot Interaction: Recent Advances and Future Perspectives

A fascinating challenge in the field of human–robot interaction is the possibility to endow robots with emotional intelligence in order to make the interaction more intuitive, genuine, and natural. To achieve this, a critical point is the capability of the robot to infer and interpret human emotions...

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Veröffentlicht in:Frontiers in robotics and AI Jg. 7; S. 532279
Hauptverfasser: Spezialetti, Matteo, Placidi, Giuseppe, Rossi, Silvia
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Switzerland Frontiers Media S.A 21.12.2020
Schlagworte:
affective computing
emotion recognition (ER)
human-robot interaction
machine learning
multimodal data
Robotics and AI
human-robot interaction
multimodal data
machine learning
affective computing
emotion recognition (ER)
ISSN:2296-9144, 2296-9144
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Zusammenfassung:A fascinating challenge in the field of human–robot interaction is the possibility to endow robots with emotional intelligence in order to make the interaction more intuitive, genuine, and natural. To achieve this, a critical point is the capability of the robot to infer and interpret human emotions. Emotion recognition has been widely explored in the broader fields of human–machine interaction and affective computing. Here, we report recent advances in emotion recognition, with particular regard to the human–robot interaction context. Our aim is to review the state of the art of currently adopted emotional models, interaction modalities, and classification strategies and offer our point of view on future developments and critical issues. We focus on facial expressions, body poses and kinematics, voice, brain activity, and peripheral physiological responses, also providing a list of available datasets containing data from these modalities.
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Edited by: Pablo Vinicius Alves De Barros, Italian Institute of Technology (IIT), Italy
Reviewed by: Bruno José Torres Fernandes, Universidade de Pernambuco, Brazil; Maya Dimitrova, Bulgarian Academy of Sciences (BAS), Bulgaria; Nicolás Navarro-Guerrero, Aarhus University, Denmark
This article was submitted to Sensor Fusion and Machine Perception, a section of the journal Frontiers in Robotics and AI
ISSN:2296-9144
2296-9144
DOI:10.3389/frobt.2020.532279