Emotion recognition based on ecg signals for service robots in the intelligent space during daily life

Kanlaya Rattanyu, Makoto Mizukawa

Research output: Contribution to journalArticle

6 Citations (Scopus)

Abstract

This paper presents our approach for emotion recognition based on Electrocardiogram (ECG) signals. We propose to use the ECG's inter-beat features together with within-beat features in our recognition system. In order toreduce the feature space, post hoc tests in the Analysis of Variance (ANOVA) were employed to select the set of eleven most significant features. We conducted experiments on twelve subjects using the International Affective Picture System (IAPS) database. RF-ECG sensors were attached to the subject's skin to monitor the ECG signal via wireless connection. Results showed that our eleven feature approach outperforms the conventional three feature approach. For simultaneous classification of six emotional states: anger, fear, disgust, sadness, neutral, and joy, the Correct Classification Ratio (CCR) showed significant improvement from 37.23% to over 61.44%. Our system was able to monitor human emotion wirelessly without affecting the subject'sactivities. Therefore it is suitable to be integrated with service robots to provide assistive and healthcare services.

Original languageEnglish
Pages (from-to)582-591
Number of pages10
JournalJournal of Advanced Computational Intelligence and Intelligent Informatics
Volume15
Issue number5
Publication statusPublished - 2011 Jul

Fingerprint

Electrocardiography
Robots
Analysis of variance (ANOVA)
Skin
Sensors
Experiments

Keywords

  • ANOVA
  • ECG
  • Emotion recognition
  • Intelligent space
  • LDA

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Vision and Pattern Recognition
  • Human-Computer Interaction

Cite this

Emotion recognition based on ecg signals for service robots in the intelligent space during daily life. / Rattanyu, Kanlaya; Mizukawa, Makoto.

In: Journal of Advanced Computational Intelligence and Intelligent Informatics, Vol. 15, No. 5, 07.2011, p. 582-591.

Research output: Contribution to journalArticle

@article{49f2b8c0b9cf4d71ba114fbb213224ac,
title = "Emotion recognition based on ecg signals for service robots in the intelligent space during daily life",
abstract = "This paper presents our approach for emotion recognition based on Electrocardiogram (ECG) signals. We propose to use the ECG's inter-beat features together with within-beat features in our recognition system. In order toreduce the feature space, post hoc tests in the Analysis of Variance (ANOVA) were employed to select the set of eleven most significant features. We conducted experiments on twelve subjects using the International Affective Picture System (IAPS) database. RF-ECG sensors were attached to the subject's skin to monitor the ECG signal via wireless connection. Results showed that our eleven feature approach outperforms the conventional three feature approach. For simultaneous classification of six emotional states: anger, fear, disgust, sadness, neutral, and joy, the Correct Classification Ratio (CCR) showed significant improvement from 37.23{\%} to over 61.44{\%}. Our system was able to monitor human emotion wirelessly without affecting the subject'sactivities. Therefore it is suitable to be integrated with service robots to provide assistive and healthcare services.",
keywords = "ANOVA, ECG, Emotion recognition, Intelligent space, LDA",
author = "Kanlaya Rattanyu and Makoto Mizukawa",
year = "2011",
month = "7",
language = "English",
volume = "15",
pages = "582--591",
journal = "Journal of Advanced Computational Intelligence and Intelligent Informatics",
issn = "1343-0130",
publisher = "Fuji Technology Press",
number = "5",

}

TY - JOUR

T1 - Emotion recognition based on ecg signals for service robots in the intelligent space during daily life

AU - Rattanyu, Kanlaya

AU - Mizukawa, Makoto

PY - 2011/7

Y1 - 2011/7

N2 - This paper presents our approach for emotion recognition based on Electrocardiogram (ECG) signals. We propose to use the ECG's inter-beat features together with within-beat features in our recognition system. In order toreduce the feature space, post hoc tests in the Analysis of Variance (ANOVA) were employed to select the set of eleven most significant features. We conducted experiments on twelve subjects using the International Affective Picture System (IAPS) database. RF-ECG sensors were attached to the subject's skin to monitor the ECG signal via wireless connection. Results showed that our eleven feature approach outperforms the conventional three feature approach. For simultaneous classification of six emotional states: anger, fear, disgust, sadness, neutral, and joy, the Correct Classification Ratio (CCR) showed significant improvement from 37.23% to over 61.44%. Our system was able to monitor human emotion wirelessly without affecting the subject'sactivities. Therefore it is suitable to be integrated with service robots to provide assistive and healthcare services.

AB - This paper presents our approach for emotion recognition based on Electrocardiogram (ECG) signals. We propose to use the ECG's inter-beat features together with within-beat features in our recognition system. In order toreduce the feature space, post hoc tests in the Analysis of Variance (ANOVA) were employed to select the set of eleven most significant features. We conducted experiments on twelve subjects using the International Affective Picture System (IAPS) database. RF-ECG sensors were attached to the subject's skin to monitor the ECG signal via wireless connection. Results showed that our eleven feature approach outperforms the conventional three feature approach. For simultaneous classification of six emotional states: anger, fear, disgust, sadness, neutral, and joy, the Correct Classification Ratio (CCR) showed significant improvement from 37.23% to over 61.44%. Our system was able to monitor human emotion wirelessly without affecting the subject'sactivities. Therefore it is suitable to be integrated with service robots to provide assistive and healthcare services.

KW - ANOVA

KW - ECG

KW - Emotion recognition

KW - Intelligent space

KW - LDA

UR - http://www.scopus.com/inward/record.url?scp=79960540813&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=79960540813&partnerID=8YFLogxK

M3 - Article

VL - 15

SP - 582

EP - 591

JO - Journal of Advanced Computational Intelligence and Intelligent Informatics

JF - Journal of Advanced Computational Intelligence and Intelligent Informatics

SN - 1343-0130

IS - 5

ER -