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Emotion Recognition Using Physiological Signals: Laboratory vs. Wearable Sensors

  • Martin RagotEmail author
  • Nicolas Martin
  • Sonia Em
  • Nico Pallamin
  • Jean-Marc Diverrez
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 608)

Abstract

Emotion recognition is an important research topic. Physiological signals seem to be an appropriate way for emotion recognition and specific sensors are required to collect these data. Therefore, laboratory sensors are commonly used while the number of wearable devices including similar physiological sensors is growing up. Many studies have been completed to evaluate the signal quality obtained by these sensors but without focusing on their emotion recognition capabilities. In the current study, Machine Learning models were trained to compare the Biopac MP150 (laboratory sensor) and Empatica E4 (wearable sensor) in terms of emotion recognition accuracy. Results show similar accuracy between data collected using laboratory and wearable sensors. These results support the reliability of emotion recognition outside laboratory.

Keywords

Wearable sensors Laboratory sensors Emotion recognition Machine learning Physiological signals 

Notes

Acknowledgments

We would like to thank all those who participated in any way in this research. This work was supported by the French government through the ANR Investment referenced ANR-10-AIRT-07.

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Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Martin Ragot
    • 1
    Email author
  • Nicolas Martin
    • 1
  • Sonia Em
    • 1
  • Nico Pallamin
    • 1
  • Jean-Marc Diverrez
    • 1
  1. 1.Usage and Acceptability Lab, b<>comCesson-SévignéFrance

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