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Real-Time Assessment of Causal Attribution Shift and Stay Between Two Successive Tests of Movement Aids

大橋 匠 渡邉 万記子 竹中 悠馬 西條 美紀 Takumi Ohashi Makiko Watanabe Yuma Takenaka Miki Saijo 東京工業大学 DOI:https://doi.org/10.1007/s12124-020-09592-7

2021.01.09

概要

The development of welfare assistive devices for frail elderly people has attracted significant attention for its effort to improve the quality of life and reduce the burden on caregivers. However, it is challenging to conduct multiple user tests because of the significant burden on the elderly; thus, we need efficient ways to extract insight through different approaches. In this study, we aim to elucidate real-time transitions in users’ emotions and achievement motivation while using such a device. We synthesize an utterance analysis method based on attribution theory, in which all users’ utterances are attributed to four categories (ability, effort, task difficulty, and luck) that follow the developed coding rules. Knowing the transitions in causal attribution allows us to extract salient experiences for users, especially by extracting shifts from them and analyzing why the shift occurred and what exactly was happening before and after the shift. If only salient user experiences can be referenced from the aggregate data, useful information can be extracted in a short time to improve system characteristics and the environment. We discussed the validity and reliability of the proposed method by conducting a user test of an electric-assisted four-wheeled cycle for frail elderly people in Kakegawa city in Shizuoka, Japan. We also succeeded in marking the points that need attention, which is about 33% of the total amount of utterance data (1626 utterances), and thus confirmed the potential of the proposed method. Future research should examine how the developed methodology can help designers improve assistive device development, as well as how it can contribute to other fields such as education and social assistance.

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参考文献

Abrilahij, A., & Boll, T. (2019). A qualitative metasynthesis of reasons for the use or nonuse of assistive technologies in the aging population. GeroPsych: The Journal of Gerontopsychology and Geriatric Psychiatry, 32(2), 79–92. https://doi.org/10.1024/1662-9647/a000203.

Agarwal, R., & Karahanna, E. (2000). Time flies when you’re having fun: cognitive absorption and beliefs about information technology usage. MIS Quarterly: Management Information Systems, 24(4), 665–694. https://doi.org/10.2307/3250951.

Alkhwaldi, A., & Kamala, M. (2017). Why do users accept innovative technologies? A critical review of models and theories of technology acceptance in the information system literature. Journal of Multidisciplinary Engineering Science and Technology (JMEST), 4(8), 7962–7971. http://www.jmest. org/wp-content/uploads/JMESTN42352358.pdf

Brandtstädter, J. (2006). Action Perspectives on Human Development. In R. M. Lerner (Ed.), Handbook of child psychology: Theoretical models of human development, 6th edition (Volume 1) (6th ed., pp. 516–568). Wiley.

Cook, D. A., & Artino, A. R. (2016). Motivation to learn: an overview of contemporary theories. Medical Education, 50(10), 997–1014. https://doi.org/10.1111/medu.13074.

Cowen, A. S., & Keltner, D. (2017). Self-report captures 27 distinct categories of emotion bridged by continuous gradients. Proceedings of the National Academy of Sciences of the United States of America, 114(38), E7900–E7909. https://doi.org/10.1073/pnas.1702247114.

Davis, F. D. (1986). A technology acceptance model for empirically testing new end-user information systems: Theory and results. MIT.

Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319. https://doi.org/10.2307/249008.

Dweck, C. S. (1975). The role of expectations and attributions in the alleviation of learned helplessness. Journal of Personality and Social Psychology, 31(4), 674–685. https://doi.org/10.1037/h0077149.

Gerber, A. J., Posner, J., Gorman, D., Colibazzi, T., Yu, S., Wang, Z., Kangarlu, A., Zhu, H., Russell, J., & Peterson, B. S. (2008). An affective circumplex model of neural systems subserving valence, arousal, and cognitive overlay during the appraisal of emotional faces. Neuropsychologia, 46(8), 2129–2139. https:// doi.org/10.1016/j.neuropsychologia.2008.02.032.

Hassenzahl, M., & Tractinsky, N. (2006). User experience - a research agenda. Behaviour & Information Technology, 25(2), 91–97. https://doi.org/10.1080/01449290500330331.

Hornbæk, K., & Hertzum, M. (2017). Technology acceptance and user experience: A review of the experiential component in HCI. ACM Transactions on Computer-Human Interaction (TOCHI), 24(5). https://doi.org/10.1145/3127358.

Jirayucharoensak, S., Pan-Ngum, S., & Israsena, P. (2014). EEG-based emotion recognition using deep learning network with principal component based covariate shift adaptation. The Scientific World Journal, 2014(7), 1–10. https://doi.org/10.1155/2014/627892.

Kawachi, I., & Berkman, L. F. (2001). Social ties and mental health. Journal of Urban Health: Bulletin of the New York Academy of Medicine, 78(3), 458–467. https://doi.org/10.1093/jurban/78.3.458.

Lai, P. (2017). The literature review of technology adoption models and theories for the novelty technology. Journal of Information Systems and Technology Management, 14(1), 21–38. https://doi.org/10.4301/ s1807-17752017000100002.

Landis, J. R., & Koch, G. G. (1977). The measurement of observer agreement for categorical data. Biometrics, 33(1), 159. https://doi.org/10.2307/2529310

Laurin, D., Verreault, R., Lindsay, J., MacPherson, K., & Rockwood, K. (2001). Physical activity and risk of cognitive impairment and dementia in elderly persons. Archives of Neurology, 58(3). https://doi.org/10. 1001/archneur.58.3.498.

Lee, Y., Kozar, K. A., & Larsen, K. R. T. (2003). The Technology Acceptance Model: Past, Present, and Future. Communications of the Association for Information Systems, 12(December). https://doi.org/10. 17705/1CAIS.01250.

Li, S., & Deng, W. (2018). Deep facial expression recognition: a survey. symmetry, 11(10), 1189. https://doi.org/10.1109/TAFFC.2020.2981446.

Lombard, M., Snyder-Duch, J., & Bracken, C. C. (2002). Content analysis in mass communication: assess- ment and reporting of intercoder reliability. Human Communication Research, 28(4), 587–604. https:// doi.org/10.1111/j.1468-2958.2002.tb00826.x.

Mohammadi, Z., Frounchi, J., & Amiri, M. (2017). Wavelet-based emotion recognition system using EEG signal.

Neural Computing and Applications, 28(8), 1985–1990. https://doi.org/10.1007/s00521-015-2149-8.

Mollahosseini, A., Hasani, B., & Mahoor, M. H. (2019). AffectNet: a database for facial expression, valence, and arousal computing in the wild. IEEE Transactions on Affective Computing, 10(1), 18–31. https://doi. org/10.1109/TAFFC.2017.2740923.

Ohashi, T., Watanabe, M., & Saijo, M. (2017). An Interaction Analysis of User-Testing to Extract Salient User Experience with the Robotic Assistive Device Life-Walker. ICRA 2017 Workshop on Advances and Challenges on the Development, Testing and Assessment of Assistive and Rehabilitation Robots: Experiences from Engineering and Human Science Research, 57–59. https://www.kau.se/files/2017-06/ ICRA2017WS-Proc-rev.pdf

Saijo, M., Watanabe, M., Aoshima, S., Oda, N., Matsumoto, S., & Kawamoto, S. (2014). Knowledge creation in technology evaluation of 4-wheel electric power assisted bicycle for frail elderly persons - a case study of a salutogenic device in healthcare facilities in Japan. Proceedings of the International Conference on Knowledge Management and Information Sharing, 87–97. https://doi.org/10.5220/0005136100870097.

Schulz, R., Wahl, H.-W., Matthews, J. T., De Vito Dabbs, A., Beach, S. R., & Czaja, S. J. (2015). Advancing the aging and technology agenda in gerontology. The Gerontologist, 55(5), 724–734. https://doi.org/10. 1093/geront/gnu071.

Sinclair, J., & Coulthard, M. (1992). ADVANCES IN Edited by Sabine Wislet-Gendebien. In M. Coulthard (Ed.), Advances in spoken discourse analysis (pp. 1–34). Routledge.

User Experience White Paper. (2011). In Seminar, Dagstuhl Experience, Demarcating User. http://www. allaboutux.org/files/UX-WhitePaper.pdf

Watanabe, M., Washio, T., Iwasaki, M., Arai, T., Saijo, M., & Ohashi, T. (2020). How effectively do experts predict elderly target-users of assistive devices? Importance of expert knowledge in device development. In A. Marcus & E. Rosenzweig (Eds.), Design, user experience, and usability. Interaction design. HCII 2020. Lecture notes in computer science (Vol. 12200, pp. 278–293). Springer, Cham. https://doi.org/10. 1007/978-3-030-49713-2_20

Weiner, B. (1985). An attributional theory of achievement motivation and emotion. Psychological Review, 92(4), 548–573. https://doi.org/10.1037/0033-295X.92.4.548.

Weiner, B. (2014). The attribution approach to emotion and motivation: history, hypotheses, home runs, headaches/heartaches. Emotion Review, 6(4), 353–361. https://doi.org/10.1177/1754073914534502.

Weiner, B., Frieze, I., Kukla, A., Reed, L., Rest, S., & Rosenbaum, R. M. (1971). Perceiving the causes of success and failure. General Learning Press.

WHO. (2015). World report on aging and health. https://www.who.int/ageing/events/world-report-2015- launch/en/

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