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Thermal infrared imaging-based affective computing and its application to facilitate human robot interaction: A review

Articolo
Data di Pubblicazione:
2020
Abstract:
Over recent years, robots are increasingly being employed in several aspects of modern society. Among others, social robots have the potential to benefit education, healthcare, and tourism. To achieve this purpose, robots should be able to engag+e humans, recognize users' emotions, and to some extent properly react and "behave" in a natural interaction. Most robotics applications primarily use visual information for emotion recognition, which is often based on facial expressions. However, the display of emotional states through facial expression is inherently a voluntary controlled process that is typical of human-human interaction. In fact, humans have not yet learned to use this channel when communicating with a robotic technology. Hence, there is an urgent need to exploit emotion information channels not directly controlled by humans, such as those that can be ascribed to physiological modulations. Thermal infrared imaging-based affective computing has the potential to be the solution to such an issue. It is a validated technology that allows the non-obtrusive monitoring of physiological parameters and from which it might be possible to infer affective states. This review is aimed to outline the advantages and the current research challenges of thermal imaging-based affective computing for human-robot interaction.
Tipologia CRIS:
1.1 Articolo in rivista
Keywords:
Affective computing; Emotion recognition; Human-robot interaction; Social robots; Thermal IR imaging
Elenco autori:
Filippini, C.; Perpetuini, D.; Cardone, D.; Chiarelli, A. M.; Merla, A.
Autori di Ateneo:
CARDONE DANIELA
CHIARELLI ANTONIO MARIA
MERLA Arcangelo
PERPETUINI DAVID
Link alla scheda completa:
https://ricerca.unich.it/handle/11564/729500
Link al Full Text:
https://ricerca.unich.it//retrieve/handle/11564/729500/234593/2020_Filippini_APPLSCIE.pdf
Pubblicato in:
APPLIED SCIENCES
Journal
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URL

https://www.mdpi.com/2076-3417/10/8/2924
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