Hostname: page-component-cd9895bd7-mkpzs Total loading time: 0 Render date: 2024-12-26T07:32:14.015Z Has data issue: false hasContentIssue false
  • English
  • Français

Enhancing the Quality of User Research Using Embedded IoT Sensors for Collecting Life Information

Published online by Cambridge University Press:  26 May 2022

T. Tanaka ,
Y. Taoka  and
S. Saito
T. Tanaka*
Affiliation:
Tokyo Institute of Technology, Japan
Y. Taoka
Affiliation:
Tokyo Institute of Technology, Japan
S. Saito
Affiliation:
Tokyo Institute of Technology, Japan
*
tanaka.t.bu@m.titech.ac.jp

Abstract

Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the ‘Save PDF’ action button.

This study aims at developing a new user research method that uses IoT sensors embedded at users' homes to enable users to recall their memories. The proposed method was evaluated by experiments where four participants individually created user journey maps with quantity data that was collected for seven days. The results showed that IoT sensor data increased the quantity, clarity, and accuracy of recalled memories. This study argues that IoT sensors can be an effective approach to increasing user research quality by triggering users' memories without interfering with users' ordinary lives.

Keywords

user researchempathyinternet of things (IoT)user-centred designdesign methods
Type
Article
Information
Proceedings of the Design Society , Volume 2: DESIGN2022 , May 2022 , pp. 613 - 622
Creative Commons
Creative Common License - CCCreative Common License - BYCreative Common License - NCCreative Common License - ND
This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives licence (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is unaltered and is properly cited. The written permission of Cambridge University Press must be obtained for commercial re-use or in order to create a derivative work.
Copyright
The Author(s), 2022.

References

Mattelmäki, T., Vaajakallio, K., & Koskinen, I. (2014), What happened to empathic design?. Design issues, 30(1), pp. 67–77. 10.1162/DESI_a_00249CrossRefGoogle Scholar
Brown, T., & Katz, B. (2009), Change by design: How design thinking transforms organizations and inspires innovation, HarperBusiness, New YorkGoogle Scholar
IDEO (Firm), & Bill & Melinda Gates Foundation. (2011), Human centered design toolkit. IDEO, California.Google Scholar
Daae, J., & Boks, C. (2015). “A classification of user research methods for design for sustainable behaviour”, Journal of Cleaner Production, 106, pp.680–689. 10.1016/j.jclepro.2014.04.056CrossRefGoogle Scholar
Van Boeijen, A., Daalhuizen, J., van der Schoor, R., & Zijlstra, J. (2014), Delft design guide: Design strategies and methods. BIS, Amsterdam.Google Scholar
Norman, D. (2013), The design of everyday things: Revised and expanded edition. Basic books, New York.Google Scholar
Larson, R., & Csikszentmihalyi, M. (2014), “The experience sampling method”, Flow and the foundations of positive psychology (pp. 21–34). Springer, Dordrecht. 10.1007/978-94-017-9088-8_2Google Scholar
Baxter, K., Courage, C., & Caine, K. (2015), Understanding your users: a practical guide to user research methods. Morgan Kaufmann, Massachusetts.Google Scholar
Hernandez, J., McDuff, D., Infante, C., Maes, P., Quigley, K., & Picard, R. (2016), “Wearable ESM: differences in the experience sampling method across wearable devices”, Proceedings of the 18th international conference on human-computer interaction with mobile devices and services, pp. 195–205. 10.1145/2935334.2935340Google Scholar
Ishio, J., & Abe, N. (2017), “Measuring affective well-being by the combination of the Day Reconstruction Method and a wearable device: Case study of an aging and depopulating community in Japan”, Augmented Human Research, 2(1), 2. 10.1007/s41133-017-0006-2CrossRefGoogle Scholar
Van Kollenburg, J., Bogers, S., Rutjes, H., Deckers, E., Frens, J., & Hummels, C. (2018), “Exploring the value of parent tracked baby data in interactions with healthcare professionals: A data-enabled design exploration”, Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems, pp. 1-12. 10.1145/3173574.3173871Google Scholar
Woo, J. B., & Lim, Y. K. (2020), “Routinoscope: Collaborative routine reflection for routine-driven do-it-yourself smart homes.” International Journal of Design, Vol. 14 No. 3, pp. 19-36. http://hdl.handle.net/10203/279527Google Scholar
Arvola, M., Blomkvist, J., & Wahlman, F. (2017), “Lifelogging in user experience research: Supporting recall and improving data richness”,The Design Journal, 20(sup1), S3954–S3965.CrossRefGoogle Scholar
Stickdorn, M., Hormess, M. E., Lawrence, A., & Schneider, J. (2018), This is service design doing: applying service design thinking in the real world, O'Reilly Media, Inc, Massachusetts.Google Scholar
Li, I., Dey, A., & Forlizzi, J. (2010), “A stage-based model of personal informatics systems”, Proceedings of the SIGCHI conference on human factors in computing systems, pp. 557–566. 10.1145/1753326.1753409Google Scholar
Oh, J., & Lee, U. (2015), “Exploring UX issues in Quantified Self technologies”, 2015 Eighth International Conference on Mobile Computing and Ubiquitous Networking (ICMU), Hakodte, Japan, Jan 20-22, 2015, IEEE pp. 53-59. 10.1109/ICMU.2015.7061028Google Scholar
Portigal, S. (2013), Interviewing users: how to uncover compelling insights, Rosenfeld Media, New York.Google Scholar