Hostname: page-component-cd9895bd7-lnqnp Total loading time: 0 Render date: 2024-12-26T05:08:30.591Z Has data issue: false hasContentIssue false

Smart design of intelligent companion toys for preschool children

Published online by Cambridge University Press:  07 December 2020

Xin Wang
Affiliation:
Faculty of Engineering, The University of Hong Kong, Hong Kong, China
Nian Yin
Affiliation:
School of Mechanical Engineering, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai200240, China
Zhinan Zhang*
Affiliation:
School of Mechanical Engineering, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai200240, China
*
Author for correspondence: Zhinan Zhang, E-mail: [email protected]

Abstract

Early childhood education has long-lasting influences on people, and an appropriate companion toy can play an essential role in children's brain development. This paper establishes a complete framework to guide the design of intelligent companion toys for preschool children from 2 to 6 years old, which is child-centered and environment-oriented. The design process is divided into three steps: requirement confirmation, the smart design before the sale, and the iterative update after the sale. This framework considers the characteristics of children and highlights the integration of human and artificial intelligence in design. A case study is provided to prove the superiority of the new framework. In addition to enriching the research on intelligent toy design, this paper also guides for practitioners to design smart toys and helps in children's cognitive development.

Type
Research Article
Copyright
Copyright © The Author(s), 2020. Published by Cambridge University Press

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Abramovici, M, Göbel, JC and Dang, HB (2016) Semantic data management for the development and continuous reconfiguration of smart products and systems. CIRP Annals – Manufacturing Technology 65, 185188. doi:10.1016/j.cirp.2016.04.051CrossRefGoogle Scholar
Abramovici, M, Göbel, JC, Savarino, P and Gebus, P (2017) Towards smart product lifecycle management with an integrated reconfiguration management. In Ríos, J, Bernard, A, Bouras, A and Foufou, S (eds), Product Lifecycle Management and the Industry of the Future. PLM 2017. IFIP Advances in Information and Communication Technology, Vol. 517. New York, Cham: Springer, LLC, pp. 489498. doi: 10.1007/978-3-319-72905-3_43Google Scholar
Alibaba (2019) Alibaba Cloud: Reliable & Secure Cloud Solutions to Empower Your Global Business. Available at https://www.alibabacloud.com/ (retrieved May 7, 2020),Google Scholar
Axelsson, M, Racca, M, Weir, D and Kyrki, V (2019) A participatory design process of a robotic tutor of assistive sign language for children with autism. In 2019 28th IEEE International Conference on Robot and Human Interactive Communication, RO-MAN 2019. Institute of Electrical and Electronics Engineers Inc. doi:10.1109/RO-MAN46459.2019.8956309.CrossRefGoogle Scholar
Banse, R and Scherer, KR (2014) Acoustic profiles in vocal emotion expression. Journal of Personality and Social Psychology 70, 614636. doi:10.1037/0022-3514.70.3.614CrossRefGoogle Scholar
Bao, Q, Faas, D and Yang, M (2018) Interplay of sketching & prototyping in early stage product design. International Journal of Design Creativity and Innovation 6, 146168. doi:10.1080/21650349.2018.1429318CrossRefGoogle Scholar
Bilal Ahmed, M, Imran Shafiq, S, Sanin, C and Szczerbicki, E (2019) Towards experience-based smart product design for industry 4.0. Cybernetics and Systems 50, 165175. doi:10.1080/01969722.2019.1565123CrossRefGoogle Scholar
Blanco, T, Casas, R, Manchado-Pérez, E, Asensio, Á and López-Pérez, JM (2017) From the islands of knowledge to a shared understanding: interdisciplinarity and technology literacy for innovation in smart electronic product design. International Journal of Technology and Design Education 27, 329362. doi:10.1007/s10798-015-9347-7CrossRefGoogle Scholar
Bouchard, K, Bouchard, B and Bouzouane, A (2012) Guidelines to efficient smart home design for rapid AI prototyping: a case study. ACM International Conference Proceeding Series. New York, NY: ACM Press, p. 1. doi:10.1145/2413097.2413134CrossRefGoogle Scholar
Cameron, D, Fernando, S, Collins, E, Millings, A, Moore, R, Sharkey, A and Prescott, T (2015) Presence of life-like robot expressions influences children's enjoyment of human-robot interactions in the field. In Proceedings of the AISB Convention 2015. The Society for the Study of Artificial Intelligence and Simulation of Behaviour.Google Scholar
Chandra, S, Alves-Oliveira, P, Lemaignan, S, Sequeira, P, Paiva, A and Dillenbourg, P (2016) Children's peer assessment and self-disclosure in the presence of an educational robot. 25th IEEE International Symposium on Robot and Human Interactive Communication, RO-MAN 2016. Institute of Electrical and Electronics Engineers Inc., pp. 539–544. doi:10.1109/ROMAN.2016.7745170CrossRefGoogle Scholar
Coelho, LdSG (2008) Aprendizagem vicária de treino de toalete através de filme de animação: estudo de caso em ludoterapia comportamental / vicarious learning in toilet training through an animated film: case study of behavioral play therapy. Psicologia Ciência e Profissão 28, 846861.CrossRefGoogle Scholar
Copple, CE and Bredekamp, S (2009) Developmentally Appropriate Practice in Early Childhood Programs Serving Children From Birth Through Age 8, 3rd Edn.. National Association for the Education of Young Children. Retrieved from: https://xueshu.baidu.com/usercenter/paper/show?paperid=0bd3f89f0e74c8fc4402c650bf169f8d&site=xueshu_seGoogle Scholar
Druga, S, Williams, R, Park, HW and Breazeal, C (2018) How smart are the smart toys? Children and parents’ agent interaction and intelligence attribution. IDC 2018 - Proceedings of the 2018 ACM Conference on Interaction Design and Children. New York, NY: Association for Computing Machinery, Inc., pp. 231–240. doi:10.1145/3202185.3202741CrossRefGoogle Scholar
Elena, MV, Wentzky, C and Summers, JD (2019) Requirements culture: a case study on product development and requirement perspectives. Proceedings of the ASME Design Engineering Technical Conference, Vol. 7. American Society of Mechanical Engineers (ASME). doi:10.1115/DETC2019-97017CrossRefGoogle Scholar
Fachada, N (2018) Teaching database concepts to video game design and development students. Revista Lusofona de Educacao 40, 7589. doi:10.24140/ISSN.1645-7250.RLE40.10Google Scholar
García, MAG, Ruiz, MLM, Rivera, D, Vadillo, L and Duboy, MAV (2017) A smart toy to enhance the decision-making process at children's psychomotor delay screenings:a pilot study. Journal of Medical Internet Research 19, e171. doi:10.2196/jmir.7533CrossRefGoogle Scholar
Gutiérrez García, MA, Martín Ruiz, ML, Rivera, D, Vadillo, L and Valero Duboy, MA (2017) A smart toy to enhance the decision-making process at children's psychomotor delay screenings: a pilot study. Journal of Medical Internet Research 19, e171. doi:10.2196/jmir.7533CrossRefGoogle Scholar
Heerink, M, Diaz, M, Albo-Canals, J, Angulo, C, Barco, A, Casacuberta, J and Garriga, C (2012) A field study with primary school children on perception of social presence and interactive behavior with a pet robot. Proceedings of the IEEE International Workshop on Robot and Human Interactive Communication, pp. 1045–1050. doi:10.1109/ROMAN.2012.6343887CrossRefGoogle Scholar
Henkemans, OAB, Bierman, BPB, Janssen, J, Looije, R, Neerincx, MA, van Dooren, MMM and Huisman, SD (2017) Design and evaluation of a personal robot playing a self-management education game with children with diabetes type 1. International Journal of Human Computer Studies 106, 6376. doi:10.1016/j.ijhcs.2017.06.001CrossRefGoogle Scholar
Horváth, I (2004) A treatise on order in engineering design research. Research in Engineering Design 15, 155181. doi:10.1007/s00163-004-0052-xCrossRefGoogle Scholar
Irani, Z, Sharif, AM, Papadopoulos, T and Love, PED (2017) Social media and Web 2.0 for knowledge sharing in product design. Production Planning and Control 28, 10471065. doi:10.1080/09537287.2017.1329955CrossRefGoogle Scholar
Janssen, JB, Van Der Wal, CC, Neerincx, MA and Looije, R (2011) Motivating children to learn arithmetic with an adaptive robot game. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 7072, LNAI. Berlin, Heidelberg: Springer, pp. 153162. doi:10.1007/978-3-642-25504-5_16Google Scholar
Jin, J, Liu, Y, Ji, P and Liu, H (2016) Understanding big consumer opinion data for market-driven product design. International Journal of Production Research 54, 30193041. doi:10.1080/00207543.2016.1154208CrossRefGoogle Scholar
Jin, J, Liu, Y, Ji, P and Kwong, CK (2019) Review on recent advances in information mining from big consumer opinion data for product design. Journal of Computing and Information Science in Engineering 19. doi:10.1115/1.4041087CrossRefGoogle Scholar
Kanda, T, Hirano, T, Eaton, D and Ishiguro, H (2011) Interactive robots as social partners and peer tutors for children: a field trial. Human–Computer Interaction, 6184. doi:10.1080/07370024.2004.9667340Google Scholar
Lazar, I, Darlington, R, Murray, H, Royce, J, Snipper, A and Ramey, CT (1982) Lasting effects of early education: a report from the consortium for longitudinal studies. Monographs of the Society for Research in Child Development 47, i. doi:10.2307/1165938CrossRefGoogle Scholar
Lin, Y, Yu, S, Zheng, P, Qiu, L, Wang, Y and Xu, X (2017) VR-based product personalization process for smart products. Procedia Manufacturing 11, 15681576. doi:10.1016/j.promfg.2017.07.297CrossRefGoogle Scholar
Lino, M, Kuczynski, K, Rodriguez, N and Schap, T (2017) Expenditures on Children by Families, 2015. Retrieved from www.cnpp.usda.govGoogle Scholar
Mourtzis, D, Doukas, M and Vandera, C (2017) Smart mobile apps for supporting product design and decision-making in the era of mass customisation. International Journal of Computer Integrated Manufacturing 30, 690707. doi:10.1080/0951192X.2016.1187295CrossRefGoogle Scholar
Nguyen, P, Nguyen, TA and Zeng, Y (2016) Quantitative analysis of the effort-fatigue tradeoff in the conceptual design process: a multistate EEG approach. ASME International. doi:10.1115/detc2016-59165Google Scholar
Poeppelbuss, J and Durst, C (2019) Smart service canvas – a tool for analyzing and designing smart product-service systems. Procedia CIRP 83, 324–329. doi:10.1016/j.procir.2019.04.077CrossRefGoogle Scholar
Quan, SJ, Park, J, Economou, A and Lee, S (2019) Artificial intelligence-aided design: smart design for sustainable city development. Environment and Planning B: Urban Analytics and City Science 46, 15811599. doi:10.1177/2399808319867946Google Scholar
Ranjan, BSC, Siddharth, L and Chakrabarti, A (2018) A systematic approach to assessing novelty, requirement satisfaction, and creativity. Artificial Intelligence for Engineering Design, Analysis and Manufacturing: aIEDAM 32, 390414. doi:10.1017/S0890060418000148CrossRefGoogle Scholar
Reich-Stiebert, N and Eyssel, F (2015) Learning with educational companion robots? Toward attitudes on education robots, predictors of attitudes, and application potentials for education robots. International Journal of Social Robotics 7, 875888. doi:10.1007/s12369-015-0308-9CrossRefGoogle Scholar
Short, E, Swift-Spong, K, Greczek, J, Ramachandran, A, Litoiu, A, Grigore, EC and Scassellati, B (2014) How to train your DragonBot: socially assistive robots for teaching children about nutrition through play. IEEE RO-MAN 2014 - 23rd IEEE International Symposium on Robot and Human Interactive Communication: Human-Robot Co-Existence: Adaptive Interfaces and Systems for Daily Life, Therapy, Assistance and Socially Engaging Interactions. Institute of Electrical and Electronics Engineers Inc., pp. 924–929. doi:10.1109/ROMAN.2014.6926371CrossRefGoogle Scholar
Summers, JD, Eckert, C and Goel, AK (2017) Function in engineering: benchmarking representations and models. Artificial Intelligence for Engineering Design, Analysis and Manufacturing: aIEDAM 31, 401412. doi:10.1017/S0890060417000476CrossRefGoogle Scholar
Tao, F, Cheng, J, Qi, Q, Zhang, M, Zhang, H and Sui, F (2018) Digital twin-driven product design, manufacturing and service with big data. International Journal of Advanced Manufacturing Technology 94, 35633576. doi:10.1007/s00170-017-0233-1CrossRefGoogle Scholar
Tozadore, D, Pinto, A, Romero, R and Trovato, G (2017) Wizard of Oz vs autonomous: Children's perception changes according to robot's operation condition. RO-MAN 2017 - 26th IEEE International Symposium on Robot and Human Interactive Communication, Vol. 2017. Institute of Electrical and Electronics Engineers Inc., pp. 664–669. doi:10.1109/ROMAN.2017.8172374CrossRefGoogle Scholar
Whiting, ME, Cagan, J and Leduc, P (2018) Efficient probabilistic grammar induction for design. Artificial Intelligence for Engineering Design, Analysis and Manufacturing: aIEDAM 32, 177188. doi:10.1017/S0890060417000464CrossRefGoogle Scholar
Wood, L, Dautenhahn, K, Robins, B and Zaraki, A (2017a) Developing child-robot interaction scenarios with a humanoid robot to assist children with autism in developing visual perspective taking skills. RO-MAN 2017 - 26th IEEE International Symposium on Robot and Human Interactive Communication, Vol. 2017. Institute of Electrical and Electronics Engineers Inc., pp. 1055–1060. doi:10.1109/ROMAN.2017.8172434CrossRefGoogle Scholar
Wood, LJ, Zaraki, A, Walters, ML, Novanda, O, Robins, B and Dautenhahn, K (2017b) The iterative development of the humanoid robot kaspar: an assistive robot for children with autism. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 10652 LNAI. Springer Verlag, pp. 5363. doi:10.1007/978-3-319-70022-9_6Google Scholar
Yang, MC (2005) A study of prototypes, design activity, and design outcome. Design Studies 26, 649669. doi:10.1016/j.destud.2005.04.005CrossRefGoogle Scholar
Yang, B, Liu, Y, Liang, Y and Tang, M (2019) Exploiting user experience from online customer reviews for product design. International Journal of Information Management 46, 173186. doi:10.1016/j.ijinfomgt.2018.12.006CrossRefGoogle Scholar
Zaraki, A, Mazzei, D, Giuliani, M and De Rossi, D (2014) Designing and evaluating a social gaze-control system for a humanoid robot. IEEE Transactions on Human-Machine Systems 44, 157168. doi:10.1109/THMS.2014.2303083CrossRefGoogle Scholar
Zeiler, W, Savanovic, P and Quanjel, EMCJ (2008) Design decision support for conceptual design. In Tools and Methods of Competitive Engineering: Proceedings of the Seventh International Symposium on Tools and Methods of Competitive Engineering (TMCE 2008). Delft University of Technology, pp. 1473–1482.Google Scholar
Zeng, Y (2011) Environment-Based Design (EBD). Proceedings of the ASME Design Engineering Technical Conference, Vol. 9. American Society of Mechanical Engineers Digital Collection, pp. 237–250. doi:10.1115/DETC2011-48263CrossRefGoogle Scholar
Zhang, Y and Kantarci, B (2019) Invited paper: Ai-based security design of mobile crowdsensing systems: review, challenges and case studies. Proceedings of the13th IEEE International Conference on Service-Oriented System Engineering, SOSE 2019, 10th International Workshop on Joint Cloud Computing, JCC 2019 and 2019 IEEE International Workshop on Cloud Computing in Robotic Systems, CCRS 2019. Institute of Electrical and Electronics Engineers Inc., pp. 17–26. doi:10.1109/SOSE.2019.00014CrossRefGoogle Scholar
Zhao, M and Zeng, Y (2019) Influence of information collection strategy on designer's mental stress. Proceedings of the International Conference on Engineering Design, ICED, Vol. 2019-Augus. Cambridge University Press, pp. 1783–1792. doi:10.1017/dsi.2019.184CrossRefGoogle Scholar
Zheng, M, She, Y, Liu, F, Chen, J, Shu, Y and Xiahou, J (2019) BabeBay - a companion robot for children based on multimodal affective computing. ACM/IEEE International Conference on Human-Robot Interaction, Vol. 2019. IEEE Computer Society, pp. 604–605. doi:10.1109/HRI.2019.8673163CrossRefGoogle Scholar