Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
Published in:
Supporting the Strategies Alignment Process in Collaborative Networks Read chapter Read first chapter

2017 | OriginalPaper | Chapter

Student’s Attention Improvement Supported by Physiological Measurements Analysis

Authors : Andreia Artífice, Fernando Ferreira, Elsa Marcelino-Jesus, João Sarraipa, Ricardo Jardim-Gonçalves

Published in: Technological Innovation for Smart Systems

Publisher: Springer International Publishing

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

The focus of the most recent theories of emotional state analysis is the Autonomic Nervous System. Those theories propose that sympathetic and parasympathetic nervous systems interact antagonistically accordingly to each emotional state implying variations of interbeat intervals of consecutive heart beats. Emotional arousal and attention can be inferred based on the electrocardiogram (ECG) specifically through Heart Rate Variability (HRV) analysis, including the Low Frequency (LF), High Frequency (HF), and ratio LF/HF. The aim of this study is to analyze the impact of classic background music, in students’ emotional arousal and attention, and performance in the context of e-Learning training courses. As a result, it is foreseen the development of a system integrating wearables to smoothly gather the mentioned biosignals, which will be able to sense user’s emotions to further automatically propose recommendations for better learning approaches and contents, aiming student’s attention improvement.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

inform now
previous chapter Data Fusion of Georeferenced Events for Detection of Hazardous Areas
next chapter A System for Driver Analysis Using Smartphone as Smart Sensor
Literature
1.
Khan, B.: Managing E-Learning: Design, Delivery, Implementation and Evaluation. Information Science Publishing, Hershey (2005)CrossRef
2.
Kaufmann, T., Sütterlin, S., Schulz, S.M., Vögele, C.: ARTiiFACT: a tool for heart rate artifact processing and heart rate variability analysis. Behav. Res. Methods 43(4), 1161–1170 (2011)CrossRef
3.
Appelhans, B.M., Luecken, L.J.: Heart rate variability as an index of regulated emotional responding. Rev. Gen. Psychol. 10(3), 229–240 (2006)CrossRef
4.
European Commission ICT Work Programme 2007-08
5.
Zarrad, A., Zaguia, A.: Building a dynamic context aware approach for smart e-learning system. In: 2015 Second International Conference on Computing Technology and Information Management (ICCTIM), pp. 144–149. IEEE (2015)
6.
Pekrun, R., Goetz, T., Titz, W., Perry, R.P.: Academic emotions in students’ self-regulated learning and achievement: a program of qualitative and quantitative research. Educ. Psychol. 37(2), 91–105 (2002)CrossRef
7.
Bahreini, K., Nadolski, R., Westera, W.: Towards multimodal emotion recognition in e-learning environments. Interact. Learn. Environ. 24(3), 590–605 (2016)CrossRef
8.
Santos, O.C., Saneiro, M., Boticario, J.G., Rodriguez-Sanchez, M.C.: Toward interactive context-aware affective educational recommendations in computer-assisted language learning. New Rev. Hypermedia Multimedia 22(1–2), 27–57 (2016)CrossRef
9.
Steele, K., Bass, K., Cook, M.: The mystery of the Mozart effect: failure to replicate. Psychol. Sci. 10(4), 366–369 (1999)CrossRef
10.
McCuntheon, L.: Another failure to generalize the Mozart effect. Pshychol. Rep. 87(1), 325–373 (2000)CrossRef
11.
Ransdell, S., Gilroy, L.: The effects of background music on word processed writing. Comput. Hum. Behav. 17, 141–148 (2001)CrossRef
12.
Kallinen, K.: Reading news from a pocket computer in a distracting environment: effects of the tempo of background music. Comput. Hum. Behav. 18, 537–551 (2002)CrossRef
13.
Thompson, W.F., Schellenberg, E.G., Hussain, G.: Arousal, mood and the Mozart effect. Pshychol. Sci. 12(3), 248–251 (2001)CrossRef
14.
Wilson, T., Brown, T.: Reexamination of the effect of Mozart’s music on spacial-task performance. J. Psychol. 131(4), 365–370 (1997)CrossRef
15.
Richards, D., Fassbender, E., Bilgin, A., Thompson, W.B.: An investigation of the role of background music in IVWs for learning. ALT-J 16(3), 231–244 (2008)CrossRef
16.
Kim, J., André, E.: Emotion recognition based on physiological changes in music listening. IEEE Trans. Pattern Anal. Mach. Intell. 30(12), 2067–2083 (2008)CrossRef
17.
Schaaff, K., Adam, M.T.P.: Measuring emotional arousal for online applications: evaluation of ultra-short term heart rate variability measures. In: Humaine Association Conference on Affective Computing and Intelligent Interaction, pp. 362–368 (2013)
18.
Kreibig, S.D.: Autonomic nervous system activity in emotion: a review. Biol. Psychol. 84, 394–421 (2010)CrossRef
19.
Katsis, C.D., Katertsidis, N., Ganiatsas, G., Fotiadis, D.I.: Towards emotion recognition in car-racing drivers: a biosignal processing approach. IEEE Trans. Syst. Man Cybern. Part A: Syst. Hum. 38(3), 502–512 (2008)CrossRef
20.
McCorry, L.K.: Physiology of the autonomic nervous system. Am. J. Pharm. Educ. 71(4), 78 (2007)CrossRef
21.
Freeman, J.V., Dewey, F.E., Hadley, D.M., Myers, J., Froelicher, V.F.: Autonomic nervous system interaction with the cardiovascular system during exercise. Prog. Cardiovasc. Dis. 48(5), 342–362 (2006)CrossRef
22.
23.
Fruhata, T., Miyachi, T., Adachi, T.: Doze driving prevention system by low frequency stimulation and high density oxygen with fragrance of GF (Grape Fruit). In: König, A., Dengel, A., Hinkelmann, K., Kise, K., Howlett, R.J., Jain, L.C. (eds.) KES 2011. LNCS (LNAI), vol. 6883, pp. 11–20. Springer, Heidelberg (2011). doi:10.​1007/​978-3-642-23854-3_​2 CrossRef
24.
Porges, S.W., Raskin, D.C.: Respiratory an heart rate components of attention. J. Exp. Psychol. 81, 497–503 (1969)CrossRef
25.
Richards, J.E., Casey, B.J.: Heart rate variability during attention phases in young infants. Psychophysiological 28, 43–53 (1991)CrossRef
26.
Porges, S.W.: Heart rate indices of newborn attentional responsivity. Merill-Palmer Quart. 20, 231–254 (1974)
27.
Tripathi, K.K., Mukundan, C., Mathew, T.L.: Attentional modulation of heart rate variability (HRV) during execution of PC based cognitive tasks. Ind. J. Aerospace Med. 47(1), 1–10 (2003)
28.
29.
Kallinen, K.: Emotional ratings of music excerpts in the western art music repertoire and their self-organization in the Kohonen neural network. Psychol. Music 33(4), 373–393 (2005)CrossRef
30.
Gião, J., Sarraipa, J., Francisco-Xavier, F., Ferreira, F., Jardim-Goncalves, R., Zdravkovic, M.: Profiling based on music and physiological state. In: Mertins, K., Jardim-Gonçalves, R., Popplewell, K., Mendonça, J.P. (eds.) Enterprise Interoperability VII. PIC, vol. 8, pp. 123–135. Springer, Cham (2016). doi:10.​1007/​978-3-319-30957-6_​10 CrossRef
Metadata
Title
Student’s Attention Improvement Supported by Physiological Measurements Analysis
Authors
Andreia Artífice
Fernando Ferreira
Elsa Marcelino-Jesus
João Sarraipa
Ricardo Jardim-Gonçalves
Copyright Year
2017
Book
Technological Innovation for Smart Systems

Print ISBN: 978-3-319-56076-2

Electronic ISBN: 978-3-319-56077-9

Copyright Year: 2017

DOI
https://doi.org/10.1007/978-3-319-56077-9_8

Premium Partner

    Image Credits