Cover not available
Article published In:
Child-Robot Interaction: Design, Evaluation, and Novel Solutions
Edited by Marta Couto, Shruti Chandra, Elmira Yadollahi and Vicky Charisi
[Interaction Studies 23:2] 2022
► pp. 157–203

Measuring mental wellbeing of children via human-robot interaction

Challenges and opportunities

| University of Cambridge
| University of Cambridge
ORCID logo | University of Cambridge
| University of Cambridge

During the last decade, children have shown an increasing need for mental wellbeing interventions due to their anxiety and depression issues, which the COVID-19 pandemic has exacerbated. Socially Assistive Robotics have been shown to have a great potential to support children with mental wellbeing-related issues. However, understanding how robots can be used to aid the measurement of these issues is still an open challenge. This paper presents a narrative review of child-robot interaction (cHRI) papers (IEEE ROMAN proceedings from 2016–2021 and keyword-based article search using Google Scholar) to investigate the open challenges and potential knowledge gaps in the evaluation of mental wellbeing or the assessment of factors affecting mental wellbeing in children. We exploited the SPIDER framework to search for the key elements for the inclusion of relevant studies. Findings from this work (10 screened papers in total) investigate the challenges in cHRI studies about mental wellbeing by categorising the current research in terms of robot-related factors (robot autonomy and type of robot), protocol-related factors (experiment purpose, tasks, participants and user sensing) and data related factors (analysis and findings). The main contribution of this work is to highlight the potential opportunities for cHRI researchers to carry out measurements concerning children’s mental wellbeing.

Keywords: children, robots, mental wellbeing assessment
Article outline
  • 1.Introduction
  • 2.Background
    • 2.1Measurements of mental wellbeing in children
    • 2.2SARs and cHRI
  • 3.Methodology
    • 3.1Term definitions
    • 3.2Search strategy
    • 3.3Selection of relevant publications
    • 3.4Data extraction and analysis
  • 4.Results
    • 4.1Functionality of the Robotised Setup
    • 4.2Protocol related limitations
    • 4.3Establishing less constrained/more naturalistic and prolonged interactions
    • 4.4Development of a validation protocol
  • 5.Challenges in cHRI
    • 5.1Functionality of the robotised setup
    • 5.2Protocol related limitations
    • 5.3Establishing longitudinal interactions
    • 5.4Development of a validation protocol
    • 5.5Ethical concerns regarding using social robots for measurements in children
    • 5.6Conducting multidisciplinary research in cHRI
  • 6.Opportunities in cHRI research for mental wellbeing
    • 6.1Using cHRI in mental wellbeing screening
    • 6.2Demographics relevant mental wellbeing screening in children
    • 6.3User sensing and cHRI experience evaluation
    • 6.4Remote robotised mental wellbeing screening
  • 7.Conclusions
  • Notes
  • References
Published online: 24 March 2023
DOI logo
https://doi.org/10.1075/is.21027.abb
References
Abbasi, N. I., Spitale, M., Anderson, J., Ford, T., Jones, P. B. & Gunes, H.
(2022) Can robots help in the evaluation of mental wellbeing in children? an empirical study. In 2022 31st IEEE International Conference on Robot and Human Interactive Communication (RO-MAN), 1459–1466. IEEE. DOI logoGoogle Scholar
Abe, K., Iwasaki, A., Nakamura, T., Nagai, T., Yokoyama, A., Shimotomai, T., Okada, H. & Omori, T.
(2012) Playmate robots that can act according to a child’s mental state. In 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems, 4660–4667. IEEE. DOI logoGoogle Scholar
Abe, K., Hamada, Y., Nagai, T., Shiomi, M. & Omori, T.
(2017) Estimation of child personality for child-robot interaction. In 2017 26th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN), 910–915. IEEE. DOI logoGoogle Scholar
Adi, Y., Killoran, A., Schrader McMillan, A. & Stewart-Brown, S.
(2007) Systematic review of interventions to promote mental wellbeing in primary schools. Natl. Inst. Heal. Clin. Excell. (NICE). Google Scholar
Alimardani, M., Kemmeren, L., Okumura, K. & Hiraki, K.
(2020) Robot-assisted mindfulness practice: Analysis of neurophysiological responses and affective state change. In 2020 29th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN), 683–689. IEEE. DOI logoGoogle Scholar
Ardito, G., Mosley, P. & Scollins, L.
(2014) We, robot: Using robotics to promote collaborative and mathematics learning in a middle school classroom. Middle Grades Res. J. 9 1. Google Scholar
Arseneault, L., Kim-Cohen, J., Taylor, A., Caspi, A. & Moffitt, T. E.
(2005) Psychometric evaluation of 5-and 7-year-old children’s self-reports of conduct problems. J. abnormal child psychology 33 1, 537–550. DOI logoGoogle Scholar
Baxter, P. E., Wood, R., Morse, A. & Belpaeme, T.
(2011) Memory-centred architectures: Perspectives on human-level cognitive competencies. In 2011 AAAI Fall Symposium Series. Google Scholar
Baxter, P. & Belpaeme, T.
(2014) Pervasive memory: The future of long-term social hri lies in the past. In Third international symposium on new frontiers in human-robot interaction at AISB. Google Scholar
Baxter, P., Ashurst, E., Kennedy, J., Senft, E., Lemaignan, S. & Belpaeme, T.
(2015) The wider supportive role of social robots in the classroom for teachers. In 1st Int. Workshop on Educational Robotics at the Int. Conf. Social Robotics. Paris, France, vol. 61. Citeseer. Google Scholar
Baxter, P., Ashurst, E., Read, R., Kennedy, J. & Belpaeme, T.
(2017) Robot education peers in a situated primary school study: Personalisation promotes child learning. PloS one 12 1, e0178126. DOI logoGoogle Scholar
Beaudoin, C. & Beauchamp, M. H.
(2020) Social cognition. In Handbook of Clinical Neurology, vol. 1731, 255–264. Elsevier. Google Scholar
Belpaeme, T., Baxter, P., Read, R., Wood, R., Cuayáhuitl, H., Kiefer, B., Racioppa, S., Kruijff-Korbayová, I., Athanasopoulos, G. & Enescu, V. et al.
(2012) Multimodal child-robot interaction: Building social bonds. J. Human-Robot Interact. 1 1. Google Scholar
Bennion, M., Hardy, G., Moore, R. & Millings, A.
(2017) E-therapies in england for stress, anxiety or depression: what is being used in the nhs? a survey of mental health services. BMJ open 7 1, e014844. DOI logoGoogle Scholar
Berger, T., Krieger, T., Sude, K., Meyer, B. & Maercker, A.
(2018) Evaluating an e-mental health program (,Äúdeprexis, Äù) as adjunctive treatment tool in psychotherapy for depression: Results of a pragmatic randomized controlled trial. J. affective disorders 227 1, 455–462. DOI logoGoogle Scholar
Berking, M. & Wupperman, P.
(2012) Emotion regulation and mental health: recent findings, current challenges, and future directions. Curr. opinion psychiatry 25 1, 128–134. DOI logoGoogle Scholar
Bethel, C. L., Stevenson, M. R. & Scassellati, B.
(2011) Secret-sharing: Interactions between a child, robot, and adult. In 2011 IEEE International Conference on systems, man, and cybernetics, 2489–2494. IEEE. DOI logoGoogle Scholar
Bethel, C. L., Henkel, Z., Stives, K., May, D. C., Eakin, D. K., Pilkinton, M., Jones, A. & Stubbs-Richardson, M.
(2016) Using robots to interview children about bullying: Lessons learned from an exploratory study. In 2016 25th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN), 712–717. IEEE. DOI logoGoogle Scholar
Bharadwaj, P., Pai, M. M. & Suziedelyte, A.
(2017) Mental health stigma. Econ. Lett. 159 1, 57–60. DOI logoGoogle Scholar
Bóo, S. J., Childs-Fegredo, J., Cooney, S., Datta, B., Dufour, G., Jones, P. B. & Galante, J.
(2020) A follow-up study to a randomised control trial to investigate the perceived impact of mindfulness on academic performance in university students. Couns. Psychother. Res. 20 1, 286–301. DOI logoGoogle Scholar
Braun, V. & Clarke, V.
(2012) Thematic analysis. DOI logoGoogle Scholar
Bremner, P., Celiktutan, O. & Gunes, H.
(2016) Personality perception of robot avatar tele-operators. In 2016 11th ACM/IEEE International Conference on Human-Robot Interaction (HRI), 141–148. IEEE. DOI logoGoogle Scholar
Broekens, J., Heerink, M., Rosendal, H. et al.
(2009) Assistive social robots in elderly care: a review. Gerontechnology 8 1, 94–103. DOI logoGoogle Scholar
Cabibihan, J.-J., Javed, H., Ang, M. & Aljunied, S. M.
(2013) Why robots? a survey on the roles and benefits of social robots in the therapy of children with autism. Int. journal social robotics 5 1, 593–618. DOI logoGoogle Scholar
Carey, S. & Markman, E. M.
(1999) Cognitive development. In Cognitive science, 201–254. Elsevier. DOI logoGoogle Scholar
Carter, R. B. & Mason, P. S.
(1998) The selection and use of puppets in counseling. Prof. Sch. Couns. 1 1, 50–53. Google Scholar
Catania, F., Spitale, M. & Garzotto, F.
(2021) Conversational agents in therapeutic interventions for neurodevelopmental disorders: A survey. ACM Comput. Surv. (CSUR). Google Scholar
Causo, A., Vo, G. T., Chen, I.-M. & Yeo, S. H.
(2016) Design of robots used as education companion and tutor. In Robotics and mechatronics, 75–84. Springer. DOI logoGoogle Scholar
Chen, H., Park, H. W. & Breazeal, C.
(2020) Teaching and learning with children: Impact of reciprocal peer learning with a social robot on children, Äôs learning and emotive engagement. Comput. & Educ. 150 1, 103836. DOI logoGoogle Scholar
Coeckelbergh, M., Pop, C., Simut, R., Peca, A., Pintea, S., David, D. & Vanderborght, B.
(2016) A survey of expectations about the role of robots in robot-assisted therapy for children with asd: ethical acceptability, trust, sociability, appearance, and attachment. Sci. engineering ethics 22 1, 47–65. DOI logoGoogle Scholar
Coninx, A., Baxter, P., Oleari, E., Bellini, S., Bierman, B., Henkemans, O., Cañamero, L., Cosi, P., Enescu, V. & Espinoza, R. et al.
(2016) Towards long-term social child-robot interaction: using multi-activity switching to engage young users. J. Human-Robot Interact.Google Scholar
Cook, A., Encarnação, P. & Adams, K.
(2010) Robots: Assistive technologies for play, learning and cognitive development. Technol. Disabil. 22 1, 127–145. DOI logoGoogle Scholar
Cooke, A., Smith, D. & Booth, A.
(2012) Beyond pico: the spider tool for qualitative evidence synthesis. Qual. health research 22 1, 1435–1443. DOI logoGoogle Scholar
Corrigan, P.
(2004) How stigma interferes with mental health care. Am. psychologist 59 1, 614. DOI logoGoogle Scholar
Cronch, L. E., Viljoen, J. L. & Hansen, D. J.
(2006) Forensic interviewing in child sexual abuse cases: Current techniques and future directions. Aggress. violent behavior 11 1, 195–207. DOI logoGoogle Scholar
Cross, E. S., Riddoch, K. A., Pratts, J., Titone, S., Chaudhury, B. & Hortensius, R.
(2019) A neurocognitive investigation of the impact of socializing with a robot on empathy for pain. Philos. Transactions Royal Soc. B 374 1, 20180034. DOI logoGoogle Scholar
De Greeff, J., Janssen, J. B., Looije, R., Mioch, T., Alpay, L., Neerincx, M. A., Baxter, P. & Belpaeme, T.
(2013) Activity switching in child-robot interaction: a hospital case study. In 5th International Conference on Social Robotics (ICSR), vol. 82391, 585–586. SPRINGER-VERLAG BERLIN. Google Scholar
de Greeff, J., Henkemans, O. B., Fraaije, A., Solms, L., Wigdor, N. & Bierman, B.
(2014) Child-robot interaction in the wild: field testing activities of the aliz-e project. In 2014 9th ACM/IEEE International Conference on Human-Robot Interaction (HRI), 148–149. IEEE. DOI logoGoogle Scholar
Deighton, J., Croudace, T., Fonagy, P., Brown, J., Patalay, P. & Wolpert, M.
(2014) Measuring mental health and wellbeing outcomes for children and adolescents to inform practice and policy: a review of child self-report measures. Child adolescent psychiatry mental health 8 1, 1–14. DOI logoGoogle Scholar
Di Nuovo, A., Varrasi, S., Lucas, A., Conti, D., McNamara, J. & Soranzo, A.
(2019) Assessment of cognitive skills via human-robot interaction and cloud computing. J. bionic engineering 16 1, 526–539. DOI logoGoogle Scholar
Diener, E. & Suh, E.
(1997) Measuring quality of life: Economic, social, and subjective indicators. Soc. indicators research 40 1, 189–216. DOI logoGoogle Scholar
Diener, E., Suh, E. M., Lucas, R. E. & Smith, H. L.
(1999) Subjective well-being: Three decades of progress. Psychol. bulletin 125 1, 276. DOI logoGoogle Scholar
Dodge, R., Daly, A. P., Huyton, J. & Sanders, L. D.
(2012) The challenge of defining wellbeing. Int. journal wellbeing 2 1. DOI logoGoogle Scholar
Druga, S., Williams, R., Breazeal, C. & Resnick, M.
(2017) “hey google is it ok if i eat you?” initial explorations in child-agent interaction. In Proceedings of the 2017 conference on interaction design and children, 595–600. DOI logoGoogle Scholar
Esteban, P. G., Cao, H.-L., De Beir, A., Van de Perre, G., Lefeber, D. & Vanderborght, B.
(2016) A multilayer reactive system for robots interacting with children with autism. arXiv preprint arXiv:1606.03875. Google Scholar
Esteban, P. G., Baxter, P., Belpaeme, T., Billing, E., Cai, H., Cao, H.-L., Coeckelbergh, M., Costescu, C., David, D. & De Beir, A. et al.
(2017) How to build a supervised autonomous system for robot-enhanced therapy for children with autism spectrum disorder. Paladyn, J. Behav. Robotics 8 1, 18–38. DOI logoGoogle Scholar
Feil-Seifer, D. & Mataric, M. J.
(2005) Defining socially assistive robotics. In 9th International Conference on Rehabilitation Robotics, 2005. ICORR 2005, 465–468. IEEE. DOI logoGoogle Scholar
Feil-Seifer, D. & Matarić, M. J.
(2009) Toward socially assistive robotics for augmenting interventions for children with autism spectrum disorders. In Experimental robotics, 201–210. Springer. DOI logoGoogle Scholar
Fischer, K. W. & Bullock, D.
(1984) Cognitive development in school-age children: Conclusions and new directions. Dev. during middle childhood: The years from six to twelve 70–146. Google Scholar
Ford, T., Vizard, T., Sadler, K., McManus, S., Goodman, A., Merad, S., Tejerina-Arreal, M., Collinson, D. & MHCYP Collaboration
(2020) Data resource profile: Mental health of children and young people (mhcyp) surveys. Int. journal epidemiology 49 1, 363–364g. DOI logoGoogle Scholar
Ford, T., John, A. & Gunnell, D.
(2021) Mental health of children and young people during pandemic. DOI logoGoogle Scholar
Gamborino, E., Yueh, H.-P., Lin, W., Yeh, S.-L. & Fu, L.-C.
(2019) Mood estimation as a social profile predictor in an autonomous, multi-session, emotional support robot for children. In 2019 28th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN), 1–6. IEEE. DOI logoGoogle Scholar
Godoi, D., Romero, R. A. F., Azevedo, H., Ramos, J., Beraldo Filho, G. & Garcia, Márcia Ap Thome
(2020) Proteger: A social robotics system to support child psychological evaluation. In 2020 Latin American Robotics Symposium (LARS), 2020 Brazilian Symposium on Robotics (SBR) and 2020 Workshop on Robotics in Education (WRE), 1–6. IEEE. DOI logoGoogle Scholar
Goldschmidt, K.
(2020) The covid-19 pandemic: Technology use to support the wellbeing of children. J. pediatric nursing 53 1, 88. DOI logoGoogle Scholar
Goris, K., Saldien, J., Vanderniepen, I. & Lefeber, D.
(2008) The huggable robot probo, a multi-disciplinary research platform. In International Conference on Research and Education in Robotics, 29–41. Springer. Google Scholar
Gustafson, D. H. et al.
(2011) An e-health solution for people with alcohol problems. Alcohol Res. & Heal. 33 1, 327. Google Scholar
Haleemunnissa, S., Didel, S., Swami, M. K., Singh, K. & Vyas, V.
(2021) Children and covid19: Understanding impact on the growth trajectory of an evolving generation. Child. youth services review 120 1, 105754. DOI logoGoogle Scholar
Hartwig, J. & Wilson, J. C.
(2002) Factors affecting children’s disclosure of secrets in an investigatory interview. Child Abus. Rev. J. Br. Assoc. for Study Prev. Child Abus. Negl. 11 1, 77–93. DOI logoGoogle Scholar
Hood, D., Lemaignan, S. & Dillenbourg, P.
(2015) When children teach a robot to write: An autonomous teachable humanoid which uses simulated handwriting. In Proceedings of the Tenth Annual ACM/IEEE International Conference on Human-Robot Interaction, 83–90. DOI logoGoogle Scholar
Ismail, L. I., Shamsudin, S., Yussof, H., Hanapiah, F. A. & Zahari, N. I.
(2012) Estimation of concentration by eye contact measurement in robot-based intervention program with autistic children. Procedia Eng. 41 1, 1548–1552. DOI logoGoogle Scholar
Jayawardena, C., Kuo, I.-H., Broadbent, E. & MacDonald, B. A.
(2014) Socially assistive robot healthbot: Design, implementation, and field trials. IEEE Syst. J. 10 1, 1056–1067. DOI logoGoogle Scholar
Kabacińska, K., Prescott, T. J. & Robillard, J. M.
(2021) Socially assistive robots as mental health interventions for children: a scoping review. Int. J. Soc. Robotics 13 1, 919–935. DOI logoGoogle Scholar
Kawaguchi, Y., Wada, K., Okamoto, M., Tsujii, T., Shibata, T. & Sakatani, K.
(2012) Investigation of brain activity after interaction with seal robot measured by fnirs. In 2012 IEEE RO-MAN: The 21st IEEE International Symposium on Robot and Human Interactive Communication, 571–576. IEEE. DOI logoGoogle Scholar
Kennedy, J., Baxter, P. & Belpaeme, T.
(2013) Constraining content in mediated unstructured social interactions: Studies in the wild. In 2013 Humaine Association Conference on Affective Computing and Intelligent Interaction, 728–733. IEEE. DOI logoGoogle Scholar
Kim, J. C., Azzi, P., Jeon, M., Howard, A. M. & Park, C. H.
(2017) Audio-based emotion estimation for interactive robotic therapy for children with autism spectrum disorder. In 2017 14th International Conference on Ubiquitous Robots and Ambient Intelligence (URAI), 39–44. IEEE. DOI logoGoogle Scholar
Komatsubara, T., Shiomi, M., Kaczmarek, T., Kanda, T. & Ishiguro, H.
(2019) Estimating children’s social status through their interaction activities in classrooms with a social robot. Int. J. Soc. Robotics 11 1, 35–48. DOI logoGoogle Scholar
Kozlova, E. A., Slobodskaya, H. R. & Gartstein, M. A.
(2020) Early temperament as a predictor of child mental health. Int. J. Mental Heal. Addict. 18 1, 1493–1506. DOI logoGoogle Scholar
Kretzschmar, K., Tyroll, H., Pavarini, G., Manzini, A., Singh, I. & NeurOx Young People’s Advisory Group
(2019) Can your phone be your therapist? young people, Äôs ethical perspectives on the use of fully automated conversational agents (chatbots) in mental health support. Biomed. informatics insights 11 1, 1178222619829083. DOI logoGoogle Scholar
Kruijff-Korbayová, I., Kiefer, B., Baroni, I. & Zelati, M. C.
(2013) Making human-robot quiz dialogue more conversational by adding non-quiz talk. In The 17th Workshop on the Semantics and Pragmatics of Dialogue (DialDam)(p. Poster). Amsterdam, The Netherlands. Google Scholar
Kumar Singh, D., Sharma, S., Shukla, J. & Eden, G.
(2020) Toy, tutor, peer, or pet? preliminary findings from child-robot interactions in a community school. In Companion of the 2020 ACM/IEEE International Conference on Human-Robot Interaction, 325–327. Google Scholar
Landry, S. H., Zucker, T. A., Taylor, H. B., Swank, P. R., Williams, J. M., Assel, M., Crawford, A., Huang, W., Clancy-Menchetti, J. & Lonigan, C. J. et al.
(2014) Enhancing early child care quality and learning for toddlers at risk: the responsive early childhood program. Dev. psychology 50 1, 526. DOI logoGoogle Scholar
Leigh, E., Chiu, K. & Clark, D. M.
(2021) Is concentration an indirect link between social anxiety and educational achievement in adolescents? PloS one 16 1, e0249952. DOI logoGoogle Scholar
Leite, I., Martinho, C., Pereira, A. & Paiva, A.
(2009) As time goes by: Long-term evaluation of social presence in robotic companions. In RO-MAN 2009-the 18th IEEE international symposium on robot and human interactive communication, 669–674. IEEE. DOI logoGoogle Scholar
Li, J.
(2015) The benefit of being physically present: A survey of experimental works comparing copresent robots, telepresent robots and virtual agents. Int. J. Human-Computer Stud. 77 1, 23–37. DOI logoGoogle Scholar
Lim, V., Rooksby, M. & Cross, E. S.
(2021) Social robots on a global stage: establishing a role for culture during human-robot interaction. Int. J. Soc. Robotics 13 1, 1307–1333. DOI logoGoogle Scholar
Lyakso, E., Frolova, O. & Grigorev, A.
(2017) Perception and acoustic features of speech of children with autism spectrum disorders. In International Conference on Speech and Computer, 602–612. Springer. DOI logoGoogle Scholar
Mak, W. W., Poon, C. Y., Pun, L. Y. & Cheung, S. F.
(2007) Meta-analysis of stigma and mental health. Soc. science & medicine 65 1, 245–261. DOI logoGoogle Scholar
Moyle, W., Cooke, M., Beattie, E., Jones, C., Klein, B., Cook, G. & Gray, C.
(2013) Exploring the effect of companion robots on emotional expression in older adults with dementia: a pilot randomized controlled trial. J. gerontological nursing 39 1, 46–53. DOI logoGoogle Scholar
Neerincx, A., Sacchitelli, F., Kaptein, R., Van Der Pal, S., Oleari, E. & Neerincx, M. A.
(2016) Child’s culture-related experiences with a social robot at diabetes camps. In 2016 11th ACM/IEEE International Conference on Human-Robot Interaction (HRI), 485–86. IEEE. DOI logoGoogle Scholar
Nock, M. K. & Kazdin, A. E.
(2001) Parent expectancies for child therapy: Assessment and relation to participation in treatment. J. Child Fam. Stud. 10 1, 155–180. DOI logoGoogle Scholar
World Health Organization et al.
(2004) Promoting mental health: Concepts, emerging evidence, practice: Summary report. World Health Organization. Google Scholar
Pachidis, T., Vrochidou, E., Kaburlasos, V. G., Kostova, S., Bonković, M. & Papić, V.
(2018) Social robotics in education: State-of-the-art and directions. In International Conference on Robotics in Alpe-Adria Danube Region, 689–700. Springer. Google Scholar
Parrish, E., Giaschi, D., Boden, C. & Dougherty, R.
(2005) The maturation of form and motion perception in school age children. Vis. Res. 45 1, 827–837. DOI logoGoogle Scholar
Pescosolido, B. A., Jensen, P. S., Martin, J. K., Perry, B. L., Olafsdottir, S. & Fettes, D.
(2008) Public knowledge and assessment of child mental health problems: Findings from the national stigma study-children. J. Am. Acad. Child & Adolesc. Psychiatry 47 1, 339–349. DOI logoGoogle Scholar
Rauchbauer, B., Nazarian, B., Bourhis, M., Ochs, M., Prévot, L. & Chaminade, T.
(2019) Brain activity during reciprocal social interaction investigated using conversational robots as control condition. Philos. Transactions Royal Soc. B 374 1, 20180033. DOI logoGoogle Scholar
Rauchbauer, B., Hmamouche, Y., Bigi, B., Prevot, L., Ochs, M. & Thierry, C.
(2020) Multimodal corpus of bidirectional conversation of human-human and human-robot interaction during fmri scanning. In Proceedings of The 12th Language Resources and Evaluation Conference, 661–668. European Language Resources Association. Google Scholar
Riek, L. D.
(2012) Wizard of oz studies in hri: a systematic review and new reporting guidelines. J. Human-Robot Interact. 1 1, 119–136. DOI logoGoogle Scholar
Rivenbark, J. G., Copeland, W. E., Davisson, E. K., Gassman-Pines, A., Hoyle, R. H., Piontak, J. R., Russell, M. A., Skinner, A. T. & Odgers, C. L.
(2019) Perceived social status and mental health among young adolescents: Evidence from census data to cellphones. Dev. psychology 55 1, 574. DOI logoGoogle Scholar
Robinette, P., Wagner, A. R. & Howard, A. M.
(2016) Assessment of robot to human instruction conveyance modalities across virtual, remote and physical robot presence. In 2016 25th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN), 1044–1050. IEEE. DOI logoGoogle Scholar
Rockhill, C. M., Fan, M.-Y., Katon, W. J., McCauley, E., Crick, N. R., Pleck, J. H.
(2007) Friendship interactions in children with and without depressive symptoms: Observation of emotion during game-playing interactions and post-game evaluations. J. abnormal child psychology 35 1, 429–441. DOI logoGoogle Scholar
Rolls, E. T.
(2019) The cingulate cortex and limbic systems for emotion, action, and memory. Brain Struct. Funct. 224 1, 3001–3018. DOI logoGoogle Scholar
Ros, R., Nalin, M., Wood, R., Baxter, P., Looije, R., Demiris, Y., Belpaeme, T., Giusti, A. & Pozzi, C.
(2011) Child-robot interaction in the wild: advice to the aspiring experimenter. In Proceedings of the 13th international conference on multimodal interfaces, 335–342. DOI logoGoogle Scholar
Ros, R. & Demiris, Y.
(2013) Creative dance: An approach for social interaction between robots and children. In International Workshop on Human Behavior Understanding, 40–51. Springer. DOI logoGoogle Scholar
Rudovic, O., Lee, J., Mascarell-Maricic, L., Schuller, B. W. & Picard, R. W.
(2017) Measuring engagement in robot-assisted autism therapy: a cross-cultural study. Front. Robotics AI 4 1, 36. DOI logoGoogle Scholar
Sabanovic, S., Michalowski, M. P. & Simmons, R.
(2006) Robots in the wild: Observing human-robot social interaction outside the lab. In 9th IEEE International Workshop on Advanced Motion Control, 2006 1., 596–601. IEEE. DOI logoGoogle Scholar
Salter, T., Werry, I. & Michaud, F.
(2008) Going into the wild in child-robot interaction studies: issues in social robotic development. Intell. Serv. Robotics 1 1, 93–108. DOI logoGoogle Scholar
Sandygulova, A. & O’Hare, G. M.
(2018) Age-and gender-based differences in children’s interactions with a gender-matching robot. Int. J. Soc. Robotics 10 1, 687–700. DOI logoGoogle Scholar
Sano, T., Horii, T., Abe, K. & Nagai, T.
(2020) Explainable temperament estimation of toddlers by a childcare robot. In 2020 29th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN), 159–164. IEEE. DOI logoGoogle Scholar
Scassellati, B., Admoni, H. & Matarić, M.
(2012) Robots for use in autism research. Annu. review biomedical engineering 14 1, 275–294. DOI logoGoogle Scholar
Shahid, S., Krahmer, E. & Swerts, M.
(2014) Child-robot interaction across cultures: How does playing a game with a social robot compare to playing a game alone or with a friend? Comput. Hum. Behav. 40 1, 86–100. DOI logoGoogle Scholar
Shi, Z., Groechel, T. R., Jain, S., Chima, K., Rudovic, O. & Matarić, M. J.
(2021) Toward personalized affect-aware socially assistive robot tutors in long-term interventions for children with autism. arXiv preprint arXiv:2101.10580. Google Scholar
Siegler, R. S.
(1994) Cognitive variability: A key to understanding cognitive development. Curr. directions psychological science 3 1, 1–5. DOI logoGoogle Scholar
Spitale, M., Silleresi, S., Cosentino, G., Panzeri, F. & Garzotto, F.
(2020) “whom would you like to talk with?” exploring conversational agents for children’s linguistic assessment. In Proceedings of the Interaction Design and Children Conference, 262–272. DOI logoGoogle Scholar
Taumoepeau, M. & Ruffman, T.
(2008) Stepping stones to others’ minds: Maternal talk relates to child mental state language and emotion understanding at 15, 24, and 33 months. Child development 79 1, 284–302. DOI logoGoogle Scholar
Thieme, A., Wallace, J., Meyer, T. D. & Olivier, P.
(2015) Designing for mental wellbeing: towards a more holistic approach in the treatment and prevention of mental illness. In Proceedings of the 2015 British HCI Conference, 1–10. DOI logoGoogle Scholar
Ting, K. L. H., Voilmy, D., Iglesias, A., Pulido, J. C., García, J., Romero-Garcés, A., Bandera, J. P., Marfil, R. & Dueñas, Á.
(2017) Integrating the users in the design of a robot for making comprehensive geriatric assessments (cga) to elderly people in care centers. In 2017 26th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN), 483–488. IEEE. DOI logoGoogle Scholar
Tozadore, D., Pinto, A., Romero, R. & Trovato, G.
(2017) Wizard of oz vs autonomous: Children’s perception changes according to robot’s operation condition. In 2017 26th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN), 664–669. IEEE. DOI logoGoogle Scholar
Tsoi, N., Connolly, J., Adéníran, E., Hansen, A., Pineda, K. T., Adamson, T., Thompson, S., Ramnauth, R., Vázquez, M. & Scassellati, B.
(2021) Challenges deploying robots during a pandemic: An effort to fight social isolation among children. In Proceedings of the 2021 ACM/IEEE International Conference on Human-Robot Interaction, 234–242. DOI logoGoogle Scholar
Uchida, T., Takahashi, H., Ban, M., Shimaya, J., Yoshikawa, Y. & Ishiguro, H.
(2017) A robot counseling system, Äîwhat kinds of topics do we prefer to disclose to robots? In 2017 26th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN), 207–212. IEEE. DOI logoGoogle Scholar
van Agteren, J., Iasiello, M., Lo, L., Bartholomaeus, J., Kopsaftis, Z., Carey, M. & Kyrios, M.
(2021) A systematic review and meta-analysis of psychological interventions to improve mental wellbeing. Nat. Hum. Behav. 5 1, 631–652. DOI logoGoogle Scholar
Van Der Drift, E. J., Beun, R.-J., Looije, R., Henkemans, O. A. B. & Neerincx, M. A.
(2014) A remote social robot to motivate and support diabetic children in keeping a diary. In 2014 9th ACM/IEEE International Conference on Human-Robot Interaction (HRI), 463–70. IEEE. DOI logoGoogle Scholar
Vanderborght, R. S., Vanderfaeillie, J., Cao, H.-L., Lefeber, D. & Vanderborght, B.
(2016) Are social robots social enough for preschoolers with autism spectrum disorder? In XI Autism Europe International Conference. Google Scholar
Venture, G., Indurkhya, B. & Izui, T.
(2017) Dance with me! child-robot interaction in the wild. In International Conference on Social Robotics, 375–382. Springer. DOI logoGoogle Scholar
Villani, V., Righi, M., Sabattini, L. & Secchi, C.
(2020) Wearable devices for the assessment of cognitive effort for human-robot interaction. IEEE Sensors J. 20 1, 13047–13056. DOI logoGoogle Scholar
Wassell, E. & Dodge, R.
(2015) A multidisciplinary framework for measuring and improving wellbeing. Int. J. Sci. Basic Appl. Res 21 1, 97–107. Google Scholar
Wellman, H. M., Song, J.-H. & Peskin-Shepherd, H.
(2019) Children’s early awareness of comprehension as evident in their spontaneous corrections of speech errors. Child Dev. 90 1, 196–209. DOI logoGoogle Scholar
Williams, R., Machado, C. V., Druga, S., Breazeal, C. & Maes, P.
(2018) “my doll says it’s ok” a study of children’s conformity to a talking doll. In Proceedings of the 17th ACM Conference on Interaction Design and Children, 625–631. DOI logoGoogle Scholar
Woodward, K., Kanjo, E., Brown, D. J. & Inkster, B.
(2020) Tangtoys: smart toys to communicate and improve children’s wellbeing. In Adjunct Proceedings of the 2020 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2020 ACM International Symposium on Wearable Computers, 497–449. DOI logoGoogle Scholar
Woolf, C., Caute, A., Haigh, Z., Galliers, J., Wilson, S., Kessie, A., Hirani, S., Hegarty, B. & Marshall, J.
(2016) A comparison of remote therapy, face to face therapy and an attention control intervention for people with aphasia: a quasi-randomised controlled feasibility study. Clin. rehabilitation 30 1, 359–373. DOI logoGoogle Scholar
Xiuqin, H., Huimin, Z., Mengchen, L., Jinan, W., Ying, Z. & Ran, T.
(2010) Mental health, personality, and parental rearing styles of adolescents with internet addiction disorder. Cyberpsychology, Behav. Soc. Netw. 13 1, 401–06. DOI logoGoogle Scholar
Yousif, J.
(2021) Social and telepresence robots a future of teaching. DOI logoGoogle Scholar
Ziemer, C. J., Wyss, S. & Rhinehart, K.
(2021) The origins of touchscreen competence: Examining infants’ exploration of touchscreens. Infant Behav. Dev. 64 1, 101609. DOI logoGoogle Scholar