Patterns of Cardiovascular and Behavioral Movements in Life-Logging According to Social Emotions

  • Hana Lee
  • Youngho Jo
  • Heajin Kim
  • Mincheol WhangEmail author
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 474)


The purpose of this study was to determine the cardiovascular and behavioral patterns to develop a new algorithm of emotion recognition system through only behavioral patterns. However, to achieve this we must compare the features with both cardiovascular responses and subjective evaluations. Seven students were asked to wear PPG sensors and carry their smartphones to track locations and periodically evaluate subjective emotions. The social emotions were categorized into mutuality and sociality dimensions. As a result, in sociality, cardiovascular features implied significant patterns in 8 cardiovascular features (p < 0.01). In mutuality, significant patterns were implied only in total power (p < 0.01). Additionally, results for sociality in behavioral features implied significant patterns in transition time and total distance (p < 0.01). Cardiovascular and behavioral patterns are two factors that can determine the physiological effects of individuals according to emotions.


Cardiovascular Behavior Social emotions Active Passive Optimistic Pessimistic 



This work was supported by the ICT R&D program of MSIP/IITP. [2015-0-00312, The development of technology for social life logging based on analyzing social emotion and intelligence of convergence contents].


  1. 1.
    McCraty, R., Atkinson, M., Tomasino, D., Bradley, R.T.: The Coherent Heart. Dana. Publication No. 06-022 (2006)Google Scholar
  2. 2.
    Szwoch, W.: Emotion recognition using physiological signals. In: Proceedings of the Mulitimedia, Interaction, Design and Innnovation on ZZZ, MIDI 2015 (2015)Google Scholar
  3. 3.
    Kim, K., Bang, S., Kim, S.: Emotion recognition system using short-term monitoring of physiological signals. Med. Biol. Eng. Comput. 42(3), 419–427 (2004)CrossRefGoogle Scholar
  4. 4.
    McCraty, R., Atkinson, M., Tiller, W.A., Rein, G., Watkins, A.D.: The effects of emotions on short-term power spectrum analysis of heart rate variability. Am. J. Cardiol. 76(14), 1089–1093 (1995). Scholar
  5. 5.
    Keselbrener, L., Akselrod, S.: Selective discrete Fourier transform algorithm for time-frequency analysis: method and application on simulated and cardiovascular signals. IEEE Trans. Biomed. Eng. 43(8), 789–802 (1996)CrossRefGoogle Scholar
  6. 6.
    Parkinson, B., Fischer, A.H., Manstead, A.S.: Emotion in Social Relations, Cultural, Group, and Interpersonal Processes. Psychology Press, New York (2005)Google Scholar
  7. 7.
    Ross, R.: A statistic for circular series. J. Educ. Psychol. 29(5), 384–389 (1938)CrossRefGoogle Scholar
  8. 8.
    Saeb, S., Zhang, M., Karr, C., Schueller, S., Corden, M., Kording, K., Mohr, D.: Mobile phone sensor correlates of depressive symptom severity in daily-life behavior: an exploratory study. J. Med. Internet Res. 17(7), e175 (2015)CrossRefGoogle Scholar
  9. 9.
    Russell, J.: A circumplex model of affect. J. Pers. Soc. Psychol. 39(6), 1161–1178 (1980)CrossRefGoogle Scholar
  10. 10.
    Sprengelmeyer, R., Rausch, M., Eysel, U., Przuntek, H.: Neural structures associated with recognition of facial expressions of basic emotions. Proc. Roy. Soc. B Biol. Sci. 265(1409), 1927–1931 (1998)CrossRefGoogle Scholar
  11. 11.
    Ekman, P.: Are there basic emotions. Psychol. Rev. 99(3), 550–553 (1992)CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  • Hana Lee
    • 1
  • Youngho Jo
    • 2
  • Heajin Kim
    • 2
  • Mincheol Whang
    • 3
    Email author
  1. 1.Department of Emotion EngineeringSangmyung UniversitySeoulRepublic of Korea
  2. 2.Team of Technology Development, Emotion Science CenterSeoulRepublic of Korea
  3. 3.Department of Intelligence Information EngineeringSangmyung UniversitySeoulRepublic of Korea

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