A Digital Diary Making System Based on User Life-Log

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10036)


A common digital diary system is a software technology that proactively suggests contents of interest to users based on various kinds of context information. It provides benefits to users and meets their satisfaction. This research was motivated by our interest in understanding the criteria for measuring the success of a diary making system from users’ point of view. Even though existing work has introduced a wide range of criteria such as users’ biological information, picture, movie, etc. In this paper, we propose a digital diary making system which aimed at measuring the user emotion from their life-log data (daily-life photos). We can get those life-log data from user’s smartphone storage. The final product of digital diary includes feeling, time, and physical location information.


Life-log Digital diary User emotion Diary making system 



This work was supported in part by the Ministry of Science, ICT and Future Planning, South Korea, Institute for Information and Communications Technology Promotion through the G-ITRC Program under Grant IITP-2015-R6812-15-0001 and in part by the National Research Foundation of Korea within the Ministry of Education, Science and Technology through the Priority Research Centers Program under Grant 2010-0020210.


  1. 1.
    Goasduff, L., Pettey, C.: Gartner says worldwide smartphone sales soared in fourth quarter of 2011 with 47 percent growth, April 2012Google Scholar
  2. 2.
    Aizawa, K., Tancharoen, D., Kawasaki, S., Yamasaki, T.: Efficient retrieval of life log based on context and content. In: Proceedings of the 1st ACM Workshop on Continuous Archival and Retrieval of Personal Experiences, pp. 22–31. ACM (2004)Google Scholar
  3. 3.
    Hori, T., Aizawa, K.: Context-based video retrieval system for the life-log applications. In: Proceedings of the 5th ACM SIGMM International Workshop on Multimedia Information Retrieval, pp. 31–38. ACM (2003)Google Scholar
  4. 4.
    Tancharoen, D., Yamasaki, T., Aizawa, K.: Practical experience recording and indexing of life log video. In: Proceedings of the 2nd ACM Won Continuous Archival and Retrieval of Personal Experiences, pp. 61–66. ACM (2005)Google Scholar
  5. 5.
    Minamikawa, A., Kotsuka, N., Honjo, M., Morikawa, D., Nishiyama, S., Ohashi, M.: Rfid supplement for mobile-based life log system. In: 2007 International Symposium on Applications and the Internet Workshops (2007)Google Scholar
  6. 6.
    Hwang, K.-S., Cho, S.-B.: Landmark detection from mobile life log using a modular Bayesian network model. Expert Syst. Appl. 36(10), 12065–12076 (2009)CrossRefGoogle Scholar
  7. 7.
    Abe, M., Morinishi, Y., Maeda, A., Aoki, M., Inagaki, H.: A life log collector integrated with a remote-controller for enabling user centric services. IEEE Trans. Cons. Electron. 55(1), 295–302 (2009)CrossRefGoogle Scholar
  8. 8.
    Ryoo, D.-W., Bae, C.: Design of the wearable gadgets for life-log services based on utc. IEEE Trans. Cons. Electron. 53(4), 1477–1482 (2007)CrossRefGoogle Scholar
  9. 9.
    Makino, Y., Murao, M., Maeno, T.: Life log system based on tactile sound. In: Kappers, A.M.L., Erp, J.B.F., Bergmann Tiest, W.M., Helm, F.C.T. (eds.) EuroHaptics 2010. LNCS, vol. 6191, pp. 292–297. Springer, Heidelberg (2010). doi: 10.1007/978-3-642-14064-8_42 CrossRefGoogle Scholar
  10. 10.
    Guo, A., Ma, J.: A smartphone-based system for personal data management and personality analysis. In: 2015 IEEE International Conference on Computer and Information Technology; Ubiquitous Computing and Communications; Dependable, Autonomic and Secure Computing; Pervasive Intelligence and Comput ing (CIT/IUCC/DASC/PICOM), pp. 2114–2122. IEEE (2015)Google Scholar
  11. 11.
    Zini, F., Reinstadler, M., Ricci, F.: Life-logs aggregation for quality of life monitoring. In: Proceedings of the 5th International Conference on Digital Health 2015, pp. 131–132. ACM (2015)Google Scholar
  12. 12.
    Machajdik, J., Hanbury, A., Garz, A., Sablatnig, R.: Affective com - puting for wearable diary and lifelogging systems: an overview. In: Machine Vision-Research for High Quality Processes and Products-35th Workshop of the Austrian Association for Pattern Recognition. Austrian Computer Society (2011)Google Scholar
  13. 13.
    Gemmell, J., Bell, G., Lueder, R.: Mylifebits: a personal database for everything. Commun. ACM 49(1), 88–95 (2006)CrossRefGoogle Scholar
  14. 14.
    Kawanishi, N., Tamai, M., Hasegawa, A., Takeuchi, Y., Tajika, A., Ogawa, Y., Furukawa, T.: Lifelog-based estimation of activity diary for cognitive behavioral therapy. In: Adjunct Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2015 ACM International Symposium on Wearable Computers, pp. 1251–1256. ACM (2015)Google Scholar
  15. 15.
    Jilek, C., Maus, H., Schwarz, S., Dengel, A.: Diary generation from personal information models to support contextual remembering and reminiscence. In: 2015 IEEE International Conference on Multimedia & Expo Workshops (ICMEW), pp. 1–6. IEEE (2015)Google Scholar
  16. 16.
    Microsoft Cognitive Services. Accessed 23 Aug 2016
  17. 17.
    Mahajan, F.R., Estrin, D.: Systemsens: a tool for monitoring usage in smartphone research deployments. In: Proceedings of the Sixth International Workshop on MobiArch, pp. 25–30. ACM (2011)Google Scholar
  18. 18.
    Android6.0 Marshmallow. Accessed 23 Aug 2016
  19. 19.
    Lopes, P.N., Salovey, P., Coté, S., Beers, M., Petty, R.E.: Emotion regulation abilities and the quality of social interaction. Emotion 5(1), 113 (2005)CrossRefGoogle Scholar
  20. 20.
    Gross, J.J., Thompson, R.A.: Emotion regulation: conceptual foundations (2007)Google Scholar

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  1. 1.College of SoftwareSungkyunkwan UniversitySuwonKorea

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