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CASIE : computing affect and social intelligence for healthcare in an ethical and trustworthy manner

(2021) PALADYN. 12. p.437-453
Author
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Abstract
This article explores the rapidly advancing innovation to endow robots with social intelligence capabilities in the form of multilingual and multimodal emotion recognition, and emotion-aware decision-making capabilities, for contextually appropriate robot behaviours and cooperative social human–robot interaction for the healthcare domain. The objective is to enable robots to become trustworthy and versatile social robots capable of having human-friendly and human assistive interactions, utilised to better assist human users’ needs by enabling the robot to sense, adapt, and respond appropriately to their requirements while taking into consideration their wider affective, motivational states, and behaviour. We propose an innovative approach to the difficult research challenge of endowing robots with social intelligence capabilities for human assistive interactions, going beyond the conventional robotic sense-think-act loop. We propose an architecture that addresses a wide range of social cooperation skills and features required for real human–robot social interaction, which includes language and vision analysis, dynamic emotional analysis (long-term affect and mood), semantic mapping to improve the robot’s knowledge of the local context, situational knowledge representation, and emotion-aware decision-making. Fundamental to this architecture is a normative ethical and social framework adapted to the specific challenges of robots engaging with caregivers and care-receivers.
Keywords
Behavioral Neuroscience, Artificial Intelligence, Cognitive Neuroscience, Developmental Neuroscience, Human-Computer Interaction

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MLA
Vasiliu, Laurentiu, et al. “CASIE : Computing Affect and Social Intelligence for Healthcare in an Ethical and Trustworthy Manner.” PALADYN, vol. 12, 2021, pp. 437–53, doi:10.1515/pjbr-2021-0026.
APA
Vasiliu, L., Cortis, K., McDermott, R., Kerr, A., Peters, A., Hesse, M., … Davis, B. (2021). CASIE : computing affect and social intelligence for healthcare in an ethical and trustworthy manner. PALADYN, 12, 437–453. https://doi.org/10.1515/pjbr-2021-0026
Chicago author-date
Vasiliu, Laurentiu, Keith Cortis, Ross McDermott, Aphra Kerr, Arne Peters, Marc Hesse, Jens Hagemeyer, et al. 2021. “CASIE : Computing Affect and Social Intelligence for Healthcare in an Ethical and Trustworthy Manner.” PALADYN 12: 437–53. https://doi.org/10.1515/pjbr-2021-0026.
Chicago author-date (all authors)
Vasiliu, Laurentiu, Keith Cortis, Ross McDermott, Aphra Kerr, Arne Peters, Marc Hesse, Jens Hagemeyer, Tony Belpaeme, John McDonald, Rudi Villing, Alessandra Mileo, Annalina Capulto, Michael Scriney, Sascha Griffiths, Adamantios Koumpis, and Brian Davis. 2021. “CASIE : Computing Affect and Social Intelligence for Healthcare in an Ethical and Trustworthy Manner.” PALADYN 12: 437–453. doi:10.1515/pjbr-2021-0026.
Vancouver
1.
Vasiliu L, Cortis K, McDermott R, Kerr A, Peters A, Hesse M, et al. CASIE : computing affect and social intelligence for healthcare in an ethical and trustworthy manner. PALADYN. 2021;12:437–53.
IEEE
[1]
L. Vasiliu et al., “CASIE : computing affect and social intelligence for healthcare in an ethical and trustworthy manner,” PALADYN, vol. 12, pp. 437–453, 2021.
@article{8728255,
  abstract     = {{This article explores the rapidly advancing innovation to endow robots with social intelligence capabilities in the form of multilingual and multimodal emotion recognition, and emotion-aware decision-making capabilities, for contextually appropriate robot behaviours and cooperative social human–robot interaction for the healthcare domain. The objective is to enable robots to become trustworthy and versatile social robots capable of having human-friendly and human assistive interactions, utilised to better assist human users’ needs by enabling the robot to sense, adapt, and respond appropriately to their requirements while taking into consideration their wider affective, motivational states, and behaviour. We propose an innovative approach to the difficult research challenge of endowing robots with social intelligence capabilities for human assistive interactions, going beyond the conventional robotic sense-think-act loop. We propose an architecture that addresses a wide range of social cooperation skills and features required for real human–robot social interaction, which includes language and vision analysis, dynamic emotional analysis (long-term affect and mood), semantic mapping to improve the robot’s knowledge of the local context, situational knowledge representation, and emotion-aware decision-making. Fundamental to this architecture is a normative ethical and social framework adapted to the specific challenges of robots engaging with caregivers and care-receivers.}},
  author       = {{Vasiliu, Laurentiu and Cortis, Keith and McDermott, Ross and Kerr, Aphra and Peters, Arne and Hesse, Marc and Hagemeyer, Jens and Belpaeme, Tony and McDonald, John and Villing, Rudi and Mileo, Alessandra and Capulto, Annalina and Scriney, Michael and Griffiths, Sascha and Koumpis, Adamantios and Davis, Brian}},
  issn         = {{2081-4836}},
  journal      = {{PALADYN}},
  keywords     = {{Behavioral Neuroscience,Artificial Intelligence,Cognitive Neuroscience,Developmental Neuroscience,Human-Computer Interaction}},
  language     = {{eng}},
  pages        = {{437--453}},
  title        = {{CASIE : computing affect and social intelligence for healthcare in an ethical and trustworthy manner}},
  url          = {{http://doi.org/10.1515/pjbr-2021-0026}},
  volume       = {{12}},
  year         = {{2021}},
}

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