CHAPTER 7. CONCLUSIONS

7.3 Limitations and Future Works

(1) Facilitate stakeholders to co-create with each other based on the activity.

We give relative stakeholders guide that the service-oriented products can follow to value-cocreate their services with suitable functions and to focus on develop the value cocreation services which are attractive to the users. They will use new integrated services to get target users enjoy their life not only from the view of health condition, but also from the view of life meaning. They now more easily understand what they can do to improve the quality of life.

(2) Facilitate wearable devices producer to pay attention to comprehensive measurement.

We build a framework about how physical data connect with activity and emotion that can give users more targeted suggestion. With the development of smart devices, users need more professional guidance behind the measurement. What is more, from the view of service science, the goal value of wearable devices is the service they can offer, not the product themselves.

(3) Facilitate baby boomers to understand their life style in a new way.

Baby boomers, which are regarded as health old people, not only can keep healthy, but also enjoy their life through our services. With the goal of wellbeing and the theory of PERMA, we try to quantify happiness in five parts - engagement and positive emotion are related simply with physical data.

7.3 Limitations and Future Works (1) The integrity of raw data

The integrity of raw data in the experiment needs to be improved. A certain number of data are lost in the process of synchronization from the wearable devices and our system. The reason is that Fitbit needs to update data manually and Spire needs to open app often to make sure app is active in background that our users often forget to open the app to do that.

(2) The veracity of algorithm and the patterns of activity and emotion

Although the result of measurement is near the actual conditions, the algorithm is still simple that needs to be improved in the future. More types of data can be added in order to distinguish more patterns of activities like work, study etc. Especially, the data of location need to be detected correctly with the help of GPS in the smart phone.

The patterns of emotion are so limited that need to be improved, too.

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(3) User experience of using wearable devices and services

Although we pick up two commercial products as our experiment devices, the experience of two wearable devices is not good enough so that it means the producers need to improve their products and related services. In the future, we can realize the data real-time function and then give real-time activity suggestions.

(4) An application interface for apps

From the suggestion of the user, it will be more attractive to them if we can package the mechanism into a standardized application interface for apps. Due to the fact that they often forget the website address and fail to open the website. Thus, an app is not only convenient for them to open, but also a reminder to them about our services.

1. Anderson, L., & Heyne, L. A. (2012). Therapeutic recreation practice: A strengths approach. Venture Pub.

2. Antheunis, M. L., Vanden Abeele, M. M., & Kanters, S. (2015). The Impact of Facebook Use on Micro-Level Social Capital: A Synthesis. Societies, 5(2), 399-419.

3. Bloch, S., Lemeignan, M., & Aguilera-T, N. (1991). Specific respiratory patterns distinguish among human basic emotions. International Journal of Psychophysiology, 11(2), 141-154.

4. De Deugd, S., Carroll, R., Kelly, K., Millett, B., & Ricker, J. (2006). SODA:

service oriented device architecture. IEEE Pervasive Computing, (3), 94-96.

5. Eakman, A. M., Carlson, M. E., & Clark, F. A. (2010). The meaningful activity participation assessment: A measure of engagement in personally valued activities. The International Journal of Aging and Human Development, 70(4), 299-317.

6. Ghosh, R., Ratan, S., Lindeman, D., & Steinmetz, V. (2013). The new era of connected aging: A framework for understanding technologies that support older adults in aging in place. Oakland, CA: Center for Technology and Aging.

7. Gilleard, C., & Higgs, P. (2008). The third age and the baby boomers: Two approaches to the social structuring of later life. International journal of ageing and later life, 2(2), 13-30

8. Grootaert, C., Narayan, D., Jones, V. N., & Woolcock, M. (2003). Integrated questionnaire for the measurement of social capital. The World Bank Social Capital Thematic Group.

9. Jerritta, S., Murugappan, M., Nagarajan, R., & Wan, K. (2011, March).

Physiological signals based human emotion recognition: a review. In Signal Processing and its Applications (CSPA), 2011 IEEE 7th International Colloquium on (pp. 410-415). IEEE.

10. Lusardi, A., & Mitchell, O. S. (2007). Baby boomer retirement security: The roles of planning, financial literacy, and housing wealth. Journal of monetary Economics, 54(1), 205-224.

11. MacInnes, J. (2006). Work–life balance in Europe: a response to the baby bust or reward for the baby boomers? European Societies, 8(2), 223-249.

12. Martin, L. G., Freedman, V. A., Schoeni, R. F., & Andreski, P. M. (2009). Health

Gerontology Series B: Psychological Sciences and Social Sciences, gbn040.

13. Mathie, M. J., Coster, A. C., Lovell, N. H., & Celler, B. G. (2004). Accelerometry:

providing an integrated, practical method for long-term, ambulatory monitoring of human movement. Physiological measurement, 25(2), R1.

14. Moraveji, N., Olson, B., Nguyen, T., Saadat, M., Khalighi, Y., Pea, R., & Heer, J.

(2011, October). Peripheral paced respiration: influencing user physiology during information work. In Proceedings of the 24th annual ACM symposium on User interface software and technology (pp. 423-428). ACM.

15. Nyan, M. N., Tay, F. E., Manimaran, M., & Seah, K. H. W. (2006, April).

Garment-based detection of falls and activities of daily living using 3-axis MEMS accelerometer. In Journal of Physics: Conference Series (Vol. 34, No. 1, p. 1059).

IOP Publishing.

16. Parasuraman, A., Zeithaml, V. A., & Berry, L. L. (1985). A conceptual model of service quality and its implications for future research. the Journal of Marketing, 41-50.

17. Park, N., Peterson, C., & Seligman, M. E. (2004). Strengths of character and well-being. Journal of social and Clinical Psychology, 23(5), 603-619.

18. Philippot, P., Chapelle, G., & Blairy, S. (2002). Respiratory feedback in the generation of emotion. Cognition & Emotion, 16(5), 605-627.

19. Porter, M. E., & Heppelmann, J. E. (2015). How Smart, Connected Products Are Transforming Companies. HARVARD BUSINESS REVIEW, 93(10), 96-+

20. Prochaska, J. O. (2008). Multiple health behavior research represents the future of preventive medicine. Preventive medicine, 46(3), 281-285.

21. Quine, S., & Carter, S. (2006). Australian baby boomers’ expectations and plans for their old age. Australasian Journal on Ageing, 25(1), 3-8.

22. Sazonov, E., & Neuman, M. R. (Eds.). (2014). Wearable Sensors: Fundamentals, implementation and applications. Elsevier

23. Segerstrom, S. C. (2001). Optimism and attentional bias for negative and positive stimuli. Personality and Social Psychology Bulletin, 27(10), 1334-1343.

24. Seligman, M. E. (2012). Flourish: A visionary new understanding of happiness and well-being. Simon and Schuster.

25. Steger, M. F., Bundick, M. J., & Yeager, D. (2012). Meaning in life. In Encyclopedia of Adolescence (pp. 1666-1677). Springer US.

‧ 國

立 政 治 大 學

N a tio na

l C h engchi U ni ve rs it y

26. Vargo, S. L., & Lusch, R. F. (2004). Evolving to a new dominant logic for marketing. Journal of marketing, 68(1), 1-17.

27. Vargo, S. L., Maglio, P. P., & Akaka, M. A. (2008). On value and value co-creation: A service systems and service logic perspective. European management journal, 26(3), 145-152

28. Vargo, S. L., & Morgan, F. W. (2005). Services in society and academic thought:

an historical analysis. Journal of Macromarketing, 25(1), 42-53.

29. Yang, C. C., & Hsu, Y. L. (2010). A review of accelerometry-based wearable motion detectors for physical activity monitoring. Sensors, 10(8), 7772-7788.

在文檔中 通過可穿戴裝置最佳化以活動為基礎的嬰兒潮顾客參與度研究 - 政大學術集成 (頁 86-90)