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Abstract

Social signal extraction from the facial analysis is
a popular research area in human-robot interaction. However,
recognition of emotional signals from Traumatic Brain Injured
(TBI) patients with the help of robots and non-intrusive sensors
is yet to be explored. Existing robots have limited abilities to
automatically identify human emotions and respond accordingly. Their interaction with TBI patients could be even more
challenging and complex due to unique, unusual and diverse
ways of expressing their emotions. To tackle the disparity
in a TBI patient’s Facial Expressions (FEs), a specialized
deep-trained model for automatic detection of TBI patients’
emotions and FE (TBI-FER model) is designed, for robotassisted rehabilitation activities. In addition, the Pepper robot’s
built-in model for FE is investigated on TBI patients as well
as on healthy people. Variance in their emotional expressions
is determined by comparative studies. It is observed that the
customized trained system is highly essential for the deployment
of Pepper robot as a Socially Assistive Robot (SAR).
Original languageEnglish
Title of host publication28th IEEE International Conference on Robot and Human Interactive Communication (ROMAN)
PublisherIEEE
Publication date12 Oct 2019
Article number8956445
ISBN (Electronic)978-1-7281-2622-7
DOIs
Publication statusPublished - 12 Oct 2019
EventThe 28th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN): Ro-man2019 - New Delhi, India
Duration: 14 Oct 201918 Nov 2019
https://ro-man2019.org/

Conference

ConferenceThe 28th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN)
CountryIndia
CityNew Delhi
Period14/10/201918/11/2019
Internet address
SeriesIEEE RO-MAN proceedings
ISSN1944-9445

Keywords

  • Robots in Education, Therapy and Rehabilitation, Non-verbal Cues and Expressiveness, Applications of Social Robots

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