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Socio-Cognitive Engineering of a Robotic Partner for Child's Diabetes Self-Management

Social or humanoid robots do hardly show up in “the wild,” aiming at pervasive and enduring human benefits such as child health. This paper presents a socio-cognitive engineering (SCE) methodology that guides the ongoing research & development for an evolving, longer-lasting human-robot partners...

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Autores principales: Neerincx, Mark A., van Vught, Willeke, Blanson Henkemans, Olivier, Oleari, Elettra, Broekens, Joost, Peters, Rifca, Kaptein, Frank, Demiris, Yiannis, Kiefer, Bernd, Fumagalli, Diego, Bierman, Bert
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7805829/
https://www.ncbi.nlm.nih.gov/pubmed/33501133
http://dx.doi.org/10.3389/frobt.2019.00118
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author Neerincx, Mark A.
van Vught, Willeke
Blanson Henkemans, Olivier
Oleari, Elettra
Broekens, Joost
Peters, Rifca
Kaptein, Frank
Demiris, Yiannis
Kiefer, Bernd
Fumagalli, Diego
Bierman, Bert
author_facet Neerincx, Mark A.
van Vught, Willeke
Blanson Henkemans, Olivier
Oleari, Elettra
Broekens, Joost
Peters, Rifca
Kaptein, Frank
Demiris, Yiannis
Kiefer, Bernd
Fumagalli, Diego
Bierman, Bert
author_sort Neerincx, Mark A.
collection PubMed
description Social or humanoid robots do hardly show up in “the wild,” aiming at pervasive and enduring human benefits such as child health. This paper presents a socio-cognitive engineering (SCE) methodology that guides the ongoing research & development for an evolving, longer-lasting human-robot partnership in practice. The SCE methodology has been applied in a large European project to develop a robotic partner that supports the daily diabetes management processes of children, aged between 7 and 14 years (i.e., Personal Assistant for a healthy Lifestyle, PAL). Four partnership functions were identified and worked out (joint objectives, agreements, experience sharing, and feedback & explanation) together with a common knowledge-base and interaction design for child's prolonged disease self-management. In an iterative refinement process of three cycles, these functions, knowledge base and interactions were built, integrated, tested, refined, and extended so that the PAL robot could more and more act as an effective partner for diabetes management. The SCE methodology helped to integrate into the human-agent/robot system: (a) theories, models, and methods from different scientific disciplines, (b) technologies from different fields, (c) varying diabetes management practices, and (d) last but not least, the diverse individual and context-dependent needs of the patients and caregivers. The resulting robotic partner proved to support the children on the three basic needs of the Self-Determination Theory: autonomy, competence, and relatedness. This paper presents the R&D methodology and the human-robot partnership framework for prolonged “blended” care of children with a chronic disease (children could use it up to 6 months; the robot in the hospitals and diabetes camps, and its avatar at home). It represents a new type of human-agent/robot systems with an evolving collective intelligence. The underlying ontology and design rationale can be used as foundation for further developments of long-duration human-robot partnerships “in the wild.”
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spelling pubmed-78058292021-01-25 Socio-Cognitive Engineering of a Robotic Partner for Child's Diabetes Self-Management Neerincx, Mark A. van Vught, Willeke Blanson Henkemans, Olivier Oleari, Elettra Broekens, Joost Peters, Rifca Kaptein, Frank Demiris, Yiannis Kiefer, Bernd Fumagalli, Diego Bierman, Bert Front Robot AI Robotics and AI Social or humanoid robots do hardly show up in “the wild,” aiming at pervasive and enduring human benefits such as child health. This paper presents a socio-cognitive engineering (SCE) methodology that guides the ongoing research & development for an evolving, longer-lasting human-robot partnership in practice. The SCE methodology has been applied in a large European project to develop a robotic partner that supports the daily diabetes management processes of children, aged between 7 and 14 years (i.e., Personal Assistant for a healthy Lifestyle, PAL). Four partnership functions were identified and worked out (joint objectives, agreements, experience sharing, and feedback & explanation) together with a common knowledge-base and interaction design for child's prolonged disease self-management. In an iterative refinement process of three cycles, these functions, knowledge base and interactions were built, integrated, tested, refined, and extended so that the PAL robot could more and more act as an effective partner for diabetes management. The SCE methodology helped to integrate into the human-agent/robot system: (a) theories, models, and methods from different scientific disciplines, (b) technologies from different fields, (c) varying diabetes management practices, and (d) last but not least, the diverse individual and context-dependent needs of the patients and caregivers. The resulting robotic partner proved to support the children on the three basic needs of the Self-Determination Theory: autonomy, competence, and relatedness. This paper presents the R&D methodology and the human-robot partnership framework for prolonged “blended” care of children with a chronic disease (children could use it up to 6 months; the robot in the hospitals and diabetes camps, and its avatar at home). It represents a new type of human-agent/robot systems with an evolving collective intelligence. The underlying ontology and design rationale can be used as foundation for further developments of long-duration human-robot partnerships “in the wild.” Frontiers Media S.A. 2019-11-15 /pmc/articles/PMC7805829/ /pubmed/33501133 http://dx.doi.org/10.3389/frobt.2019.00118 Text en Copyright © 2019 Neerincx, van Vught, Blanson Henkemans, Oleari, Broekens, Peters, Kaptein, Demiris, Kiefer, Fumagalli and Bierman. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Robotics and AI
Neerincx, Mark A.
van Vught, Willeke
Blanson Henkemans, Olivier
Oleari, Elettra
Broekens, Joost
Peters, Rifca
Kaptein, Frank
Demiris, Yiannis
Kiefer, Bernd
Fumagalli, Diego
Bierman, Bert
Socio-Cognitive Engineering of a Robotic Partner for Child's Diabetes Self-Management
title Socio-Cognitive Engineering of a Robotic Partner for Child's Diabetes Self-Management
title_full Socio-Cognitive Engineering of a Robotic Partner for Child's Diabetes Self-Management
title_fullStr Socio-Cognitive Engineering of a Robotic Partner for Child's Diabetes Self-Management
title_full_unstemmed Socio-Cognitive Engineering of a Robotic Partner for Child's Diabetes Self-Management
title_short Socio-Cognitive Engineering of a Robotic Partner for Child's Diabetes Self-Management
title_sort socio-cognitive engineering of a robotic partner for child's diabetes self-management
topic Robotics and AI
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7805829/
https://www.ncbi.nlm.nih.gov/pubmed/33501133
http://dx.doi.org/10.3389/frobt.2019.00118
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