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Older Adults’ Voice Search through the Human-Engaged Computing Perspective

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HCI International 2021 - Late Breaking Posters (HCII 2021)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1498))

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Abstract

Human-Engaged Computing (HEC) is a framework that addresses “synergized interaction” sustaining both humans and computers in the right balance, a relationship that consciously honors human inner capabilities over device creativity. Due to the growing interest and demand on voice search for older adults, it is critical to research on how to engage older adults with voice search to improve their healthiness and wellbeing. This paper presents two case studies to discuss the approaches and thoughts about applying HEC to the current voice search systems, in particular, how HEC can engage older adults with interaction of voice search systems and how we can measure older adults’ engagement with such systems.

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References

  1. Attfield, S., Kazai, G., Lalmas, M., Piwowarski, B.: Towards a science of user engagement (position paper). In: WSDM Workshop on User Modelling for Web Applications, pp. 9–12 (2011)

    Google Scholar 

  2. Census: By 2030, All Baby Boomers Will Be Age 65 or Older (2019). https://www.census.gov/library/stories/2019/12/by-2030-all-baby-boomers-will-be-age-65-or-older.html#:~:text=Since%20then%2C%20about%2010%2C000%20a,of%20the%20U.S.%20Census%20Bureau. Accessed 30 Apr 2021

  3. Chaudhuri, S., Le, T., White, C., Thompson, H., Demiris, G.: Examining health information–seeking behaviors of older adults. Comput. Informat. Nurs. CIN. 31(11), 547–553 (2013)

    Article  Google Scholar 

  4. Esposito, A., et al.: The dependability of voice on elders’ acceptance of humanoid agents. In: INTERSPEECH, pp. 31–35 (2019)

    Google Scholar 

  5. Georgila, K., Wolters, M., Moore, J.D., Logie, R.H.: The MATCH corpus: a corpus of older and younger users’ interactions with spoken dialogue systems. Lang. Resour. Eval. 44(3), 221–261 (2010)

    Article  Google Scholar 

  6. Guy, I.: Searching by talking: Analysis of voice queries on mobile web search. In: Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval, pp. 35–44, July, 2016

    Google Scholar 

  7. Kobayashi, M., et al.: Effects of age-related cognitive decline on elderly user interactions with voice-based dialogue systems. In: Lamas, D., Loizides, F., Nacke, L., Petrie, H., Winckler, M., Zaphiris, P. (eds.) INTERACT 2019. LNCS, vol. 11749, pp. 53–74. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-29390-1_4

    Chapter  Google Scholar 

  8. Kowalski, J., et al.: Older adults and voice interaction: a pilot study with google home. In: Extended Abstracts of the 2019 CHI Conference on human factors in computing systems, pp. 1–6 (2019)

    Google Scholar 

  9. Li, J., Maharjan, B., Xie, B., Tao, C.: A personalized voice-based diet assistant for caregivers of Alzheimer disease and related dementias: system development and validation. J. Med. Internet Res. 22(9), e19897 (2020)

    Google Scholar 

  10. Ma, X.: Towards human-engaged AI. In: IJCAI, pp. 5682–5686 (2018)

    Google Scholar 

  11. Möller, S., Gödde, F., Wolters, M.: A corpus analysis of spoken smart-home interactions with older users. In: Proceedings of the 6th International Conference on Language Resources and Evaluation (LREC), Marrakech, Morocco, pp. 735–740 (2008)

    Google Scholar 

  12. Niksirat, K.S., Silpasuwanchai, C., Cheng, P., Ren, X.: Attention regulation framework: designing self-regulated mindfulness technologies. ACM Trans. Comput. Hum. Inter. (TOCHI) 26(6), 1–44 (2019). https://doi.org/10.1145/3359593

    Article  Google Scholar 

  13. Niksirat, K.S., Silpasuwanchai, C., Mohamed Hussien Ahmed, M., Cheng, P., Ren, X.: A framework for interactive mindfulness meditation using attention-regulation process. In: Twelve Agendas on Interacting with Information: A Human-Engaged Computing Perspective 199 Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems, pp. 2672–2684. Association for Computing Machinery, New York (2017). https://doi.org/10.1145/3025453.3025914

  14. Ren, X., Silpasuwanchai, C., Cahill, J.: Human-engaged computing: the future of human–computer interaction. CCF Trans. Perv. Comput. Inter. 1(1), 47–68 (2019). https://doi.org/10.1007/s42486-019-00007-0

    Article  Google Scholar 

  15. Sa, N., Yuan, X.-J.: Examining users’ partial query modification patterns in voice search. J. Am. Soc. Inf. Sci. Technol. 71(3), 251–263 (2020) (cover article). First published on 29 April 2019. https://doi.org/10.1002/asi.24238

  16. Sa, N., Yuan, X.-J.: Challenges in conversational search: improving the system capabilities and guiding the search process. In: Proceedings of the 24th World Multi-Conference on Systemics, Cybernetics and Informatics (WMSCI 2020), pp. 37–42 (2020)

    Google Scholar 

  17. Sa, N., Yuan, X.-J.: Examining user perception and usage of voice search. J. Data Inf. Manag. 5(1), 1–14 (2021)

    Google Scholar 

  18. Sa, N., Yuan, X.-J.: Improving voice search system effectiveness through partial query modification. J. Am. Soc. Inform. Sci. Technol. (JASIST), (2021) (under review, major revision)

    Google Scholar 

  19. Sidner, C.L., Lee, C., Kidd, C.D., Lesh, N., Rich, C.: Explorations in engagement for humans and robots. Artif. Intell. 166(1–2), 140–164 (2005). https://doi.org/10.1016/j.artint.2005.03.005

  20. Vandemeulebroucke, T., de Casterlé, B.D., Gastmans, C.: How do older adults experience and perceive socially assistive robots in aged care: a systematic review of qualitative evidence. Aging Ment. Health 22(2), 149–167 (2018)

    Article  Google Scholar 

  21. Wang, C., et al.: Approaching aesthetics on user interface and interaction design. In: Proceedings of the 2018 ACM International Conference on Interactive Surfaces and Spaces, pp. 481–484. Association for Computing Machinery, New York, November 2018. https://doi.org/10.1145/3279778.3279809

  22. Wang, C., Yuan, X.J., Ren, X.: Twelve agendas on interacting with information: a human-engaged computing perspective. Data Inf. Manag. 4(3), 191–199 (2020)

    Google Scholar 

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Acknowledgments

We thank the Initiative for Women (IFW) Endowment Award.

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Correspondence to Xiaojun (Jenny) Yuan .

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Yuan, X., Ren, X. (2021). Older Adults’ Voice Search through the Human-Engaged Computing Perspective. In: Stephanidis, C., Antona, M., Ntoa, S. (eds) HCI International 2021 - Late Breaking Posters. HCII 2021. Communications in Computer and Information Science, vol 1498. Springer, Cham. https://doi.org/10.1007/978-3-030-90176-9_39

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  • DOI: https://doi.org/10.1007/978-3-030-90176-9_39

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