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 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.”

[1]  den Pj Perry Brok,et al.  Let's make things better! Developments in research on interpersonal relationships in education. , 2012 .

[2]  L. S. Vygotskiĭ,et al.  Mind in society : the development of higher psychological processes , 1978 .

[3]  Neerincx,et al.  Situated cognitive engineering for complex task environments , 2005 .

[4]  A. Pai,et al.  Treatment Adherence in Adolescents and Young Adults Affected by Chronic Illness During the Health Care Transition From Pediatric to Adult Health Care: A Literature Review , 2011 .

[5]  Anthony F. Grasha,et al.  A Matter of Style: The Teacher as Expert, Formal Authority, Personal Model, Facilitator, and Delegator , 1994 .

[6]  Tina Mioch,et al.  Improving Adaptive Human-Robot Cooperation through Work Agreements , 2018, 2018 27th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN).

[7]  Joris B. Janssen,et al.  Design and evaluation of a personal robot playing a self-management education game with children with diabetes type 1 , 2017, Int. J. Hum. Comput. Stud..

[8]  Mark A. Neerincx Situated cognitive engineering for crew support in space , 2010, Personal and Ubiquitous Computing.

[9]  Maja J. Mataric,et al.  Month-long, In-home Case Study of a Socially Assistive Robot for Children with Autism Spectrum Disorder , 2018, HRI.

[10]  Dale H. Schunk,et al.  Sex Differences in Self-Efficacy and Attributions: Influence of Performance Feedback , 1984 .

[11]  Jonathan Grudin,et al.  Design and evaluation , 1995 .

[12]  Bert P. B. Bierman,et al.  Friendship with a robot: Children's perception of similarity between a robot's physical and virtual embodiment that supports diabetes self-management. , 2018, Patient education and counseling.

[13]  Donald S. Strassberg,et al.  Self-Disclosure Reciprocity among Preadolescents , 1983 .

[14]  Koen V. Hindriks,et al.  Personalised self-explanation by robots: The role of goals versus beliefs in robot-action explanation for children and adults , 2017, 2017 26th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN).

[15]  Colonel (ret) Beth Ellen Davis,et al.  Parental Wartime Deployment and the Use of Mental Health Services Among Young Military Children , 2010, Pediatrics.

[16]  M. Birna van Riemsdijk,et al.  Socially adaptive electronic partners for improved support of children's values: An empirical study with a location-sharing mobile app , 2018, Int. J. Child Comput. Interact..

[17]  Neelam Naikar,et al.  Cognitive work analysis: An influential legacy extending beyond human factors and engineering. , 2017, Applied ergonomics.

[18]  Philippe A. Palanque,et al.  Design, specification, and verification of interactive systems , 2000, Proceedings of the 2000 International Conference on Software Engineering. ICSE 2000 the New Millennium.

[19]  E. Deci,et al.  Self-Determination Theory: Basic Psychological Needs in Motivation, Development, and Wellness , 2017 .

[20]  Sylvie Naar-King,et al.  The role of parental monitoring in adolescent health outcomes: impact on regimen adherence in youth with type 1 diabetes. , 2007, Journal of pediatric psychology.

[21]  K. J. Vicente,et al.  Cognitive Work Analysis: Toward Safe, Productive, and Healthy Computer-Based Work , 1999 .

[22]  Sangdo Han,et al.  Counseling Dialog System with 5W1H Extraction , 2013, SIGDIAL Conference.

[23]  Cynthia Breazeal,et al.  Machine behaviour , 2019, Nature.

[24]  Mark A. Neerincx,et al.  Robots Expressing Dominance: Effects of Behaviours and Modulation , 2019, 2019 8th International Conference on Affective Computing and Intelligent Interaction (ACII).

[25]  Koen V. Hindriks,et al.  CAAF: A Cognitive Affective Agent Programming Framework , 2016, IVA.

[26]  Yiannis Demiris,et al.  Ontologies for Social, Cognitive and Affective Agent-Based Support of Child’s Diabetes Self Management , 2016, ECAI 2016.

[27]  S. Chaiklin The zone of proximal development in Vygotsky's analysis of learning and instruction. , 2003 .

[28]  E. Thoma Interpersonal Diagnosis of Personality , 1965 .

[29]  Mark A. Neerincx,et al.  Integrating Robot Support Functions into Varied Activities at Returning Hospital Visits , 2016, International Journal of Social Robotics.

[30]  Emilie M. Roth,et al.  Cognitive Engineering: Human Problem Solving with Tools , 1988 .

[31]  Mark A. Neerincx,et al.  Developing Effective and Resilient Human-Agent Teamwork Using Team Design Patterns , 2019, IEEE Intelligent Systems.

[32]  Yiannis Demiris,et al.  Online Knowledge Level Tracking with Data-Driven Student Models and Collaborative Filtering , 2020, IEEE Transactions on Knowledge and Data Engineering.

[33]  Mark A. Neerincx,et al.  Usability of the PAL Objectives Dashboard for Children's Diabetes Self-Management Education , 2019, Proceedings of the 2019 the 5th International Conference on e-Society, e-Learning and e-Technologies - ICSLT 2019.

[34]  A. Bandura Self-efficacy: toward a unifying theory of behavioral change. , 1977, Psychological review.

[35]  Cristiano André da Costa,et al.  Survey of conversational agents in health , 2019, Expert Syst. Appl..

[36]  R. Iannotti,et al.  Self-Efficacy, Outcome Expectations, and Diabetes Self-Management in Adolescents with Type 1 Diabetes , 2006, Journal of developmental and behavioral pediatrics : JDBP.

[37]  Alex Barco,et al.  Can social robots help children in healthcare contexts? A scoping review , 2019, BMJ Paediatrics Open.

[38]  Thomas R. Gruber,et al.  A translation approach to portable ontology specifications , 1993, Knowl. Acquis..

[39]  J. Tudge,et al.  The cognitive consequences of collaborative problem solving with and without feedback , 1996 .

[40]  Daniel P. Davison,et al.  Towards a child-robot symbiotic co-development: a theoretical approach , 2015, HRI 2015.

[41]  Mark A. Neerincx,et al.  Using Perceptual and Cognitive Explanations for Enhanced Human-Agent Team Performance , 2018, HCI.

[42]  Allison Druin,et al.  The design of children's technology , 1998 .

[43]  Michael Beetz,et al.  Robot recommender system using affection-based episode ontology for personalization , 2013, 2013 IEEE RO-MAN.

[44]  Ginevra Castellano,et al.  Adaptive Robotic Tutors that Support Self-Regulated Learning: A Longer-Term Investigation with Primary School Children , 2018, Int. J. Soc. Robotics.

[45]  L. Vygotsky Mind in Society: The Development of Higher Psychological Processes: Harvard University Press , 1978 .

[46]  B. Boyer,et al.  Comprehensive handbook of clinical health psychology , 2008 .

[47]  Laurel D. Riek,et al.  Healthcare robotics , 2017, Commun. ACM.

[48]  Cynthia Breazeal,et al.  Affective Personalization of a Social Robot Tutor for Children's Second Language Skills , 2016, AAAI.

[49]  BrinkmanWillem-Paul,et al.  Automatic Resolution of Normative Conflicts in Supportive Technology Based on User Values , 2018 .

[50]  Mark A. Neerincx,et al.  Robots educate in style: The effect of context and non-verbal behaviour on children's perceptions of warmth and competence , 2017, 2017 26th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN).

[51]  Il Hong Suh,et al.  Ontology-Based Unified Robot Knowledge for Service Robots in Indoor Environments , 2011, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[52]  J. Forgas Mood and judgment: the affect infusion model (AIM). , 1995, Psychological bulletin.

[53]  Susan L. Epstein Wanted: Collaborative intelligence , 2015, Artif. Intell..

[54]  P Betts,et al.  The care of young people with diabetes. , 1996, Diabetic medicine : a journal of the British Diabetic Association.

[55]  J. Broekens,et al.  Let Me Guide You ! Pedagogical Interaction Style for a Robot in Children ’ s Education , 2015 .

[56]  Mark A. Neerincx,et al.  Ontology Engineering for the Design and Implementation of Personal Pervasive Lifestyle Support , 2016, SEMANTiCS.

[57]  Xin Sun,et al.  Mobile App-Based Interventions to Support Diabetes Self-Management: A Systematic Review of Randomized Controlled Trials to Identify Functions Associated with Glycemic Efficacy , 2017, JMIR mHealth and uHealth.

[58]  Davide Calvaresi,et al.  Explainable Agents and Robots: Results from a Systematic Literature Review , 2019, AAMAS.

[59]  Mark A. Neerincx,et al.  Learning with Charlie: A robot buddy for children with diabetes , 2017, HRI.

[60]  J. Singer,et al.  Cognitive, social, and physiological determinants of emotional state. , 1962, Psychological review.

[61]  Neerincx,et al.  Co-design of the PAL robot and avatar that perform joint activities with children for improved diabetes self-management , 2016, RO-MAN 2016.

[62]  Mark A. Neerincx,et al.  Fostering Relatedness Between Children and Virtual Agents Through Reciprocal Self-disclosure , 2016, BNCAI.

[63]  Mark A. Neerincx,et al.  Interaction Design Patterns for Adaptive Human-Agent-Robot Teamwork in High-Risk Domains , 2016, HCI.

[64]  Erik Hollnagel,et al.  Joint Cognitive Systems: Foundations of Cognitive Systems Engineering , 2005 .

[65]  M. Heerink,et al.  Social robots to support children’s well-being under medical treatment: A systematic state-of-the-art review , 2018, Journal of child health care : for professionals working with children in the hospital and community.

[66]  Alessandro Bogliolo,et al.  The Rise of Bots: A Survey of Conversational Interfaces, Patterns, and Paradigms , 2017, Conference on Designing Interactive Systems.

[67]  Alonso H. Vera,et al.  No AI Is an Island: The Case for Teaming Intelligence , 2019, AI Mag..

[68]  K. Rotenberg,et al.  Development of the reciprocity of self-disclosure. , 1992, The Journal of genetic psychology.

[69]  R. Ryan,et al.  Self-determination theory , 2015 .

[70]  Allison Druin,et al.  The role of children in the design of new technology , 2002 .

[71]  Nicole L. Robinson,et al.  Psychosocial Health Interventions by Social Robots: Systematic Review of Randomized Controlled Trials , 2019, Journal of medical Internet research.

[72]  A.,et al.  Cognitive Engineering , 2008, Encyclopedia of GIS.

[73]  Maarten Sierhuis,et al.  Human-agent-robot teamwork , 2012, 2012 7th ACM/IEEE International Conference on Human-Robot Interaction (HRI).

[74]  Mingwei Jin,et al.  Chronic conditions in adolescents , 2017, Experimental and therapeutic medicine.

[75]  Jens Rasmussen,et al.  Cognitive Systems Engineering , 2022 .

[76]  Gerrit C. van der Veer,et al.  An ontology for task models , 1998 .

[77]  Cynthia Breazeal,et al.  Measuring young children's long-term relationships with social robots , 2018, IDC.

[78]  Marie-Christine Opdenakker,et al.  Let’s Make Things Better , 2012 .

[79]  Hella Haanstra,et al.  Opinion aspect extraction in Dutch childrens diary entries , 2019, ArXiv.

[80]  Pieter Abbeel,et al.  Image Object Label 3 D CAD Model Candidate Grasps Google Object Recognition Engine Google Cloud Storage Select Feasible Grasp with Highest Success Probability Pose EstimationCamera Robots Cloud 3 D Sensor , 2014 .

[81]  Koen V. Hindriks,et al.  Specifying and testing the design rationale of social robots for behavior change in children , 2017, Cognitive Systems Research.

[82]  Mike Sharples,et al.  Socio-cognitive engineering: A methodology for the design of human-centred technology , 2002, European Journal of Operational Research.

[83]  P. Darbyshire,et al.  Multiple methods in qualitative research with children: more insight or just more? , 2005 .

[84]  Rhett Iseman,et al.  In the Care , 1998 .

[85]  M. Birna van Riemsdijk,et al.  Automatic Resolution of Normative Conflicts in Supportive Technology Based on User Values , 2018, ACM Trans. Internet Techn..