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Wemotion: A System to Detect Emotion Using Wristbands and Smartphones

  • Bao-Lan Le-QuangEmail author
  • Minh-Son Dao
  • Mohamed Saleem Haja Nazmudeen
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 968)

Abstract

Understanding students’ emotion, especially during the classroom time, can help to improve the positive classroom emotional climate towards promoting academic achievement. Unfortunately, most of the exisiting studies that try to understand the emotion of students have just utilized a questionnaire method to discover the link between the classroom emotional climate and academic achievement. Such methods do not reflect exactly the emotion of students in the real-time mode. There are also other studies that leverage hi-tech technologies (e.g. video camera, sensors, smartphones) to capture data generated by people themselves (e.g. physiological data, facial expression, body postures, human-smartphone interaction) to recognize emotion. Nonetheless, these methods build either a general model for all users or an individual model for a selected user leading to having a weak adaptive ability. In this paper, we introduce Wemotion, a smart-sensing system built by smartphones and wristbands that can not only detect students’ emotion in real-time mode and also evolve to continuously improve the accuracy during the life cycle. The system is evaluated by real data collected from volunteers and compared to several existing methods. The results show that the system works well and satisfies the purpose of our research.

Keywords

Emotion detection Wearable sensor Smartphone Ubiquitous ambient Machine learning Adaptive learning 

References

  1. 1.
    Pekrun, R.: The impact of emotions on learning and achievement: towards a theory of cognitive/motivational mediators. Appl. Psychol. Int. Rev. 41(4), 359–376 (1992)CrossRefGoogle Scholar
  2. 2.
    Bryan, T., Mathur, S., Sullivan, K.: The impact of positive mood on learning. Learn. Disabil. Q. 19(3), 153–162 (1996)CrossRefGoogle Scholar
  3. 3.
    Febrilia, I., Warokka, A.: The effects of positive and negative mood on university students’ learning and academic performance: evidence from indonesia. In: The 3rd International Conference on Humanities and Social Sciences, pp. 1–12 (2011)Google Scholar
  4. 4.
    Sottilare, R.A., Proctor, M.: Passively classifying student mood and performance within intelligent tutors. J. Educ. Technol. Soc. 15(2), 101 (2012)Google Scholar
  5. 5.
    Lewine, R., Sommers, A., Waford, R., Robertson, C.: Setting the mood for critical thinking in the classroom. Int. J. Sch. Teach. Learn. 9(2), 1–4 (2015)Google Scholar
  6. 6.
    Liew, T.W., Tan, S.-M.: The effects of positive and negative mood on cognition and motivation in multimedia learning environment. Educ. Technol. Soci. 19(2), 104–115 (2016)Google Scholar
  7. 7.
    Kulkarni, S.S., Reddy, N.P., Hariharan, S.I.: Facial expression (mood) recognition from facial images using committee neural networks. BioMed. Eng. OnLine 8, 16 (2009)CrossRefGoogle Scholar
  8. 8.
    Moridis, C.N., Economides, A.A.: Mood recognition during online self-assessment tests. IEEE Trans. Learn. Technol. 2(1), 50–61 (2009)CrossRefGoogle Scholar
  9. 9.
    Thrasher, M., Van der Zwaag, M.D., Bianchi-Berthouze, N., Westerink, J.H.D.M.: Mood recognition based on upper body posture and movement features. In: D’Mello, S., Graesser, A., Schuller, B., Martin, J.-C. (eds.) ACII 2011. LNCS, vol. 6974, pp. 377–386. Springer, Heidelberg (2011).  https://doi.org/10.1007/978-3-642-24600-5_41CrossRefGoogle Scholar
  10. 10.
    Gjoreski, M., Gjoreski, H., Lutrek, M., Gams, M.: Automatic detection of perceived stress in campus students using smartphones. In: 2015 International Conference on Intelligent Environments (IE), pp. 132–135. IEEE (2015)Google Scholar
  11. 11.
    Maaoui, C., Pruski, A.: Emotion recognition through physiological signals for human-machine communication. In: Cutting Edge Robotics, pp. 317–333. INTECH Open Access Publisher (2010)Google Scholar
  12. 12.
    LiKamWa, R., Liu, Y., Lane, N.D., Zhong, L.: MoodScope: building a mood sensor from smartphone usage patterns. In: Proceeding of the 11th Annual International Conference on Mobile Systems, Applications, and Services, pp. 389–402. ACM (2013)Google Scholar
  13. 13.
    Odinaka, I., et al.: ECG biometric recognition: a comparative analysis. IEEE Trans. Inf. Forensics Secur. 7(6), 1812–1824 (2012)CrossRefGoogle Scholar
  14. 14.
    Tapia, E.M., et al.: Real-time recognition of physical activities and their intensities using wireless accelerometers and a heart rate monitor. In: 11th IEEE International Symposium on Wearable Computers. IEEE (2007)Google Scholar
  15. 15.
    Sebe, N., et al.: Multimodal approaches for emotion recognition: a survey. In: Internet Imaging VI, vol. 5670. International Society for Optics and Photonics (2005)Google Scholar
  16. 16.
    Vinola, C., Vimaladevi, K.: A survey on human emotion recognition approaches, databases and applications. ELCVIA Electr. Lett. Comput. Vis. Image Anal. 14(2), 24–44 (2015)CrossRefGoogle Scholar
  17. 17.
    Sharma, N., Tom, G.: Objective measures, sensors and computational techniques for stress recognition and classification: a survey. Comput. Methods Programs Biomed. 108(3), 1287–1301 (2012)CrossRefGoogle Scholar
  18. 18.
    Basu, S., et al.: A review on emotion recognition using speech. In: 2017 International Conference on Inventive Communication and Computational Technologies (ICICCT). IEEE (2017)Google Scholar
  19. 19.
    Wioleta, S.: Using physiological signals for emotion recognition. In: 6th International Conference on Human System Interactions (HSI), pp. 556–561. IEEE (2013)Google Scholar
  20. 20.
    McDuff, D., Gontarek, S., Picard, R.: Remote measurement of cognitive stress via heart rate variability. In: 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (2014)Google Scholar
  21. 21.
  22. 22.
    Alexandratos, V.: Mobile Real-Time Stress Detection (2014)Google Scholar
  23. 23.
    Leys, C., et al.: Detecting outliers: do not use standard deviation around the mean, use absolute deviation around the median. J. Exp. Soc. Psychol. 49(4), 764–766 (2013)CrossRefGoogle Scholar
  24. 24.
    Healey, J.A.: Detecting stress during real-world driving tasks using physiological sensors. IEEE Trans. Intell. Transp. Syst. 6(2), 156–166 (2005)CrossRefGoogle Scholar
  25. 25.
    Sano, A., Picard, R.W.: Stress recognition using wearable sensors and mobile phones. In: 2013 Humaine Association Conference on Affective Computing and Intelligent Interaction (ACII). IEEE (2013)Google Scholar
  26. 26.
    Zenonos, A., et al.: HealthyOffice: mood recognition at work using smartphones and wearable sensors. In: 2016 IEEE International Conference on Pervasive Computing and Communication Workshops (PerCom Workshops). IEEE (2016)Google Scholar
  27. 27.
    Wijsman, J., et al.: Wearable physiological sensors reflect mental stress state in office-like situations. In: 2013 Humaine Association Conference on Affective Computing and Intelligent Interaction (ACII). IEEE (2013)Google Scholar
  28. 28.
    Valenza, G., et al.: Wearable monitoring for mood recognition in bipolar disorder based on history-dependent long-term heart rate variability analysis. IEEE J. Biomed. Health Inf. 18(5), 1625–1635 (2014)CrossRefGoogle Scholar
  29. 29.
    Reyes, C.R., et al.: Classroom emotional climate, student engagement, and academic achievement. J. Educ. Psychol. 104(3), 700–712 (2012)CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Bao-Lan Le-Quang
    • 1
    Email author
  • Minh-Son Dao
    • 2
  • Mohamed Saleem Haja Nazmudeen
    • 2
  1. 1.University of Information Technology Ho Chi Minh CityHo Chi Minh CityVietnam
  2. 2.Universiti Teknologi BruneiGadongBrunei Darussalam

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