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Emotion Recognition Based on Physiological Sensor Data Using Codebook Approach

  • Kimiaki ShirahamaEmail author
  • Marcin Grzegorzek
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 472)

Abstract

This paper addresses emotion recognition based on physiological signal sequences (e.g., blood pressure, galvanic skin response and respiration) that can be obtained using state-of-the-art wearable sensors. We formulate this as a machine learning problem to distinguish sequences labelled with a certain emotion from the other sequences. In particular, we explore how to extract a feature that effectively characterises a sequence and yields accurate emotion recognition. With respect to this, existing methods rely on hand-crafted features that are manually defined based on prior knowledge about physiological signals. However, in addition to intensive labour, it is difficult to manually design features which can represent the details of a sequence. To overcome this, we propose a codebook approach where a sequence is represented with a feature describing the distribution of characteristic subsequences, called codewords. These are statistically justified because they are obtained by clustering a large number of subsequences. In addition, the details of the sequence can be maintained by considering the distribution of hundreds of codewords. Experimental results validate the effectiveness of our codebook-based emotion recognition method.

Keywords

Ambient assisted living Emotion recognition Physiological sensor data Codebook approach Sequence classification 

Notes

Acknowledgments

Research and development activities leading to this article have been supported by the German Federal Ministry of Education and Research within the project Cognitive Village: Adaptively Learning Technical Support System for Elderly (Grant Number: 16SV7223K).

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Pattern Recognition GroupUniversity of SiegenSiegenGermany

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