Abstract
Due to global digitalisation, teaching in virtual reality is becoming a growing market. Compared to learning in class, individual learning scenarios are possible. To find out, if a person is currently stressed or overstrained and the training course thus should be adapted, it is necessary to detect the emotional state of the person. Therefore in this paper a sensor headband is introduced, which is able to measure certain physiological values such as galvanic skin conductance, blood volume pulse or body temperature. With the help of feature extraction it is then possible to determine, which emotional state relevant in learning scenarios is predominate.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Alshamsi, H., Meng, H., Li, M.: Real time facial expression recognition app development on mobile phones. In: 12th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD) (2016). https://doi.org/10.1109/FSKD.2016.7603442
Das, P., Khasnobish, A., Tibarewala, D.: Emotion recognition employing ECG and GSR signals as markers of ANS. In: Conference on Advances in Signal Processing (CASP) (2016). https://doi.org/10.1109/CASP.2016.7746134
ELISE: Elise-förderprojekt - lebenslanges lernen mit gefühl, spaß und technik (2016). http://elise-lernen.de/. (in German)
Fink, C.: VR Training Next Generation of Workers (2017). https://www.forbes.com/sites/charliefink/2017/10/30/vr-training-next-generation-of-workers
Guo, S., Cai, X., Gao, B., Yuhua, J.: An improved VR training system for vascular interventional surgery. In: IEEE International Conference on Robotics and Biomimetics (2016). https://doi.org/10.1109/ROBIO.2016.7866567
hackaday.io: Serialplot - Realtime Plotting Software (2017). https://hackaday.io/project/5334-serialplot-realtime-plotting-software
Maxim Integrated: Datenblatt: Max30102. https://datasheets.maximintegrated.com/en/ds/MAX30102.pdf. Accessed 05 Dec 2017
Melexis: Datasheet: Mlx90614. https://www.melexis.com/-/media/files/documents/datasheets/mlx90614-datasheet-melexis.pdf. Accessed 05 Dec 2017
Nguyen, B.T., Trinh, M.H., Phan, T.V., Nguyen, H.D.: An efficient real-time emotion detection using camera and facial landmarks. In: Seventh International Conference on Information Science and Technology (ICIST) (2017). https://doi.org/10.1109/ICIST.2017.7926765
Quazi, M.T., Mukhopadhyay, S.C., Suryadevara, N.K., Huang, Y.M.: Towards the smart sensors based human emotion recognition. In: IEEE International Instrumentation and Measurement Technology Conference (I2MTC) (2012). https://doi.org/10.1109/I2MTC.2012.6229646
de Ribaupierre, S., Armstrong, R., Noltie, D., Kramers, M., Eagleson, R.: VR and AR simulator for neurosurgical training. In: IEEE Virtual Reality (VR) (2015). https://doi.org/10.1109/VR.2015.7223338
Torres-Valencia, C.A., Alvarez, M.A., Orozco-Gutierrez, A.A.: Multiple-output support vector machine regression with feature selection for arousal/valence space emotion assessment. In: 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (2014). https://doi.org/10.1109/EMBC.2014.6943754
Vinay, G.S., Mehra, A.: Gender specific emotion recognition through speech signals. In: International Conference on Signal Processing and Integrated Networks (SPIN) (2014). https://doi.org/10.1109/SPIN.2014.6777050
Wiem, M.B.H., Lachiri, Z.: Emotion recognition system based on physiological signals with raspberry pi III implementation. In: 3rd International Conference on Frontiers of Signal Processing (ICFSP) (2017). https://doi.org/10.1109/ICFSP.2017.8097053
youtube.com: The Most Boring Video Ever \(|\) Deleted Scenes (2016). https://www.youtube.com/watch?v=s34zGmq3rXQ
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer International Publishing AG, part of Springer Nature
About this paper
Cite this paper
Krönert, D., Grünewald, A., Li, F., Grzegorzek, M., Brück, R. (2019). Sensor Headband for Emotion Recognition in a Virtual Reality Environment. In: Pietka, E., Badura, P., Kawa, J., Wieclawek, W. (eds) Information Technology in Biomedicine. ITIB 2018. Advances in Intelligent Systems and Computing, vol 762. Springer, Cham. https://doi.org/10.1007/978-3-319-91211-0_47
Download citation
DOI: https://doi.org/10.1007/978-3-319-91211-0_47
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-91210-3
Online ISBN: 978-3-319-91211-0
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)