Thought and Life Logging: A Pilot Study

  • N. HernándezEmail author
  • G. Yavuz
  • R. Eşrefoğlu
  • T. Kepez
  • A. Özdemir
  • B. Demiray
  • H. Alan
  • C. Ersoy
  • S. Untersander
  • B. Arnrich
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9454)


Thought and Life Logging (Tholilo) is an interdisciplinary research project of computer engineers and psychologists. One aspect of Tholilo is to understand how daily context influence our mood and temporal thinking. In this contribution, we present a data collection framework which records sensor data and survey responses from smartphones to infer user’s context, user’s mood and temporal thinking. In a pilot study, data is collected from two collectives located in Turkey and in Switzerland. We examine correlations between phone data and surveys. As a proof of concept, we show how phone data is correlated with changes in participant’s mood. We conclude with lessons learned and future work.


Mobile sensing Lifestyle Physical activity Social interaction 



We thank our participants from Turkey and from Switzerland. With your valuable feedback we will start with the second round of data collection in autumn 2015.

This work was partially funded by the Co-Funded Brain Circulation Scheme Project “Pervasive Healthcare: Towards Computational Networked Life Science” (TÜBITAK Co-Circ 2236, Grant agreement number: 112C005) supported by TÜBİTAK and EC FP7 Marie Curie Action COFUND.


  1. 1.
    Yohan, C. L., Nicholas, D., Li, F., Cha, H., Zhao, F.: Automatically characterizing places with opportunistic crowdsensing using smartphones. In: Proceedings of the 2012 ACM Conference on Ubiquitous Computing, pp 481–490 (2012)Google Scholar
  2. 2.
    Demiray, B., Mehl, M., Martin, M.: How much do people talk about their future in everyday life? A naturalistic observation study. Poster presented at the International Conference on Prospective Memory, Naples, Italy (2014)Google Scholar
  3. 3.
    Giordano, S., Puccinelli, D.: When sensing goes pervasive. Pervasive Mob. Comput. 17, 175–183 (2015). Part BCrossRefGoogle Scholar
  4. 4.
    Brajdic, A., Harle, R.: Walk detection and step counting on unconstrained smartphones. In: Proceedings of the 2013 ACM International Joint Conference on Pervasive and Ubiquitous Computing, UbiComp 2013 (2013)Google Scholar
  5. 5.
    Miluzzo, E., Lane, N., Fodor, K., Peterson, R., Lu, H., Musolesi, M., Eisenman, S., Zheng, X., Campbell, A.: Sensing meets mobile social networks: the design, implementation and evaluation of the CenceMe application. In: Proceedings of the International Conference on Embedded Networked Sensor Systems, SenSys 2008 (2008)Google Scholar
  6. 6.
    Miluzzo, E., Papandrea, M., Lane, N.D., Sarroff, A.M., Giordano, S., Campbell, A.T.: Tapping into the vibe of the city using vibn, a continuous sensing application for smartphones. In: Proceedings of First International Symposium on Social and Community Intelligence, SCI 2011. Beijing, China (2011)Google Scholar
  7. 7.
    Arnrich, B., Erdem, N.Ş, Alan, H.F., Ersoy, C.: Sensing healthy lifestyle in urban and rural environments. In: 3rd International Conference on Context-Aware Systems and Applications (2014)Google Scholar
  8. 8.
    Muaremi, A., Bexheti, A., Gravenhorst, F., Seiter, J., Feese, S., Arnrich, B., Tröster, G.: Understanding aspects of pilgrimage using social networks derived from smartphones. Pervasive Mob. Comput. 15, 166–180 (2014)CrossRefGoogle Scholar
  9. 9.
    Muaremi, A., Gravenhorst, F., Grünerbl, A., Arnrich, B., Tröster, G.: Assessing bipolar episodes using speech cues derived from phone calls. In: Cipresso, P., Matic, A., Lopez, G. (eds.) MindCare 2014. LNICST, vol. 100, pp. 103–114. Springer, Heidelberg (2014)Google Scholar
  10. 10.
    Muaremi, A., Arnrich, B., Tröster, G.: Towards measuring stress with smartphones and wearable devices during workday and sleep. BioNanoScience 3(2), 172–183 (2013)CrossRefGoogle Scholar
  11. 11.
    Feese, S., Arnrich, B., Rossi, M., Tröster, G., Burtscher, M., Meyer, B., Jonas, K.: Towards monitoring firefighting teams with the smartphone. In: IEEE International Conference on Pervasive Computing and Communications (PERCOM), pp 381–384 (2013)Google Scholar
  12. 12.
    Seiter, J., Macrea, L., Feese, S., Amft, O., Arnrich, B., Maurer, K., Tröster, G.: Activity monitoring in daily life as outcome measure for surgical pain relief intervention using smartphones. In: 17th International Symposium on Wearable Computers (ISWC), pp 127–128 (2013)Google Scholar
  13. 13.
    Seiter, J., Feese, S., Arnrich, B., Tröster, G., Amft, O., Macrea, L., Maurer, K.: Evaluating daily life activity using smartphones as novel outcome measure for surgical pain therapy. In: 8th International Conference on Body Area Networks (BodyNets), pp 153–156 (2013)Google Scholar
  14. 14.
    Feese, S., Arnrich, B., Tröster, G., Burtscher, M., Meyer, B., Jonas, K.: CoenoFire: monitoring performance indicators of firefighters in real-world missions using smartphones. In: ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp 2013), pp 83–92 (2013)Google Scholar
  15. 15.
    Feese, S., Arnrich, B., Tröster, G., Burtscher, M., Meyer, B., Jonas, K.: Sensing group proximity dynamics of firefighting teams using smartphones. In: International Symposium on Wearable Computers (ISWC), pp 97–104 (2013)Google Scholar
  16. 16.
    Muaremi, A., Arnrich, B., Tröster, G.: A survey on measuring happiness with smart phones. In: 6th International Workshop on Ubiquitous Health and Wellness (UbiHealth) (2012)Google Scholar
  17. 17.
    Madan, A., Cebrian, M., Lazer, D., Pentland, A.: Social sensing for epidemiological behavior change, pp. 291–300 (2010)Google Scholar
  18. 18.
    Silva, T.H., de Melo, P.O.S.V., Almeida, J.M., Salles, J., Loureiro, A.A.F.: Revealing the city that we cannot see. ACM Trans. Internet Technol. 14(4), 1–23 (2014)CrossRefGoogle Scholar
  19. 19.
    Eagle, N., Pentland, A.S.: Reality mining: sensing complex social systems. Pers. Ubiquitous Comput. 10(4), 255–268 (2005)CrossRefGoogle Scholar
  20. 20.
    Both, F., Hoogendoorn, M., Klein, M.C.A., Treur, J.: Computational modeling and analysis of the role of physical activity in mood regulation and depression. In: Neural Information Processing: Theory and Algorithm, pp. 270–281 (2010)Google Scholar
  21. 21.
    Aharony, N., Gardner, A., Sumter, C., Pentland, A.: Funf: Open Sensing Framework. (2011)

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • N. Hernández
    • 1
    Email author
  • G. Yavuz
    • 2
  • R. Eşrefoğlu
    • 2
  • T. Kepez
    • 2
  • A. Özdemir
    • 2
  • B. Demiray
    • 3
  • H. Alan
    • 2
  • C. Ersoy
    • 2
  • S. Untersander
    • 3
  • B. Arnrich
    • 2
  1. 1.Computer Science DepartmentCICESE Research CenterEnsenadaMexico
  2. 2.Computer Engineering DepartmentBoğaziçi UniversityIstanbulTurkey
  3. 3.Psychology DepartmentUniversity of ZürichZurichSwitzerland

Personalised recommendations