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Emotion Recognition Through Cardiovascular Response in Daily Life Using KNN Classifier

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Advances in Computer Science and Ubiquitous Computing (CUTE 2017, CSA 2017)

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 474))

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Abstract

Emotion in daily life is difficult to recognize due to disadvantageous of continuous measurement. This study was to develop the method for recognizing daily emotion from a measurement of daily cardiovascular response by using the developed wireless sensor. Seven subjects assessed subjective emotions based on Russell’s emotional circumplex model every 3 h wearing a photo-plethysmography (PPG) sensor. The heart rate variability (HRV) according to two emotional dimensions were tested by the Kruskal-Wallis test. Significant parameters of them were determined to be distinguished among emotions and were applied to recognize emotions using the K-Nearest Neighbor (KNN) algorithm. The arousal and valence were recognized with respective 88.2% and 56.2% accuracy. The methods in this study is extended to monitor and recognized in industrial domain and health care domain requiring recognition of long-term emotion.

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Acknowledgments

This work was supported by Institute for Information & communications Technology Promotion (IITP) grant funded by the Korea government (MSIT) (No. 2015-0-00312, The development of technology for social life logging based on analyzing social emotion and intelligence of convergence contents).

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Authors and Affiliations

  1. Department of Emotion Engineering, Sangmyung University, Hongji-dong, Jongno-gu, Seoul, 03016, Republic of Korea

    Youseop Jo, Hyunwoo Lee & Ayoung Cho

  2. Department of Intelligence Information Engineering, Sangmyung University, Hongji-dong, Jongno-gu, Seoul, 03016, Republic of Korea

    Mincheol Whang

Authors
  1. Youseop Jo
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  2. Hyunwoo Lee
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  3. Ayoung Cho
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  4. Mincheol Whang
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Corresponding author

Correspondence to Mincheol Whang .

Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering, Seoul University of Science and Technology, Seoul, Korea (Republic of)

    James J. Park

  2. Department of Business Science, University of Salerno, Salerno, Italy

    Vincenzo Loia

  3. Department of Multimedia Engineering, Dongguk University, Seoul, Soul-t’ukpyolsi, Korea (Republic of)

    Gangman Yi

  4. Department of Multimedia Engineering, Dongguk University, Seoul, Soul-t’ukpyolsi, Korea (Republic of)

    Yunsick Sung

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Jo, Y., Lee, H., Cho, A., Whang, M. (2018). Emotion Recognition Through Cardiovascular Response in Daily Life Using KNN Classifier. In: Park, J., Loia, V., Yi, G., Sung, Y. (eds) Advances in Computer Science and Ubiquitous Computing. CUTE CSA 2017 2017. Lecture Notes in Electrical Engineering, vol 474. Springer, Singapore. https://doi.org/10.1007/978-981-10-7605-3_231

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  • DOI: https://doi.org/10.1007/978-981-10-7605-3_231

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-7604-6

  • Online ISBN: 978-981-10-7605-3

  • eBook Packages: EngineeringEngineering (R0)

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