Abstract

For some time, equine assisted therapy (EAT), i.e. the use of horse-related activities for therapeutic reasons, has been recognised as a useful approach in the treatment of many mental health issues such as post-traumatic stress disorder (PTSD), depression, and anxiety. However, despite the interest in EAT, few scientific studies have focused on understanding the complex emotional response that horses seem to elicit in human riders and handlers. In this work, the potential use of affect recognition techniques based on physiological signals is examined for the task of assessing the interaction between humans and horses in terms of the emotional response of the humans to this interaction. Electroencephalography (EEG), electrocardiography (ECG), and electromyography (EMG) signals were captured from humans interacting with horses, and machine learning techniques were applied in order to predict the self-reported emotional states of the human subjects in terms of valence and arousal. Supervised classification experiments demonstrated the potential of this approach for affect recognition during human-horse interaction, reaching an F1-score of 78.27% for valence and 65.49% for arousal.
Original languageEnglish
Pages (from-to)77857-77867
Number of pages11
JournalIEEE Access
Volume7
DOIs
Publication statusPublished - 10 Jun 2019

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Electromyography
Electroencephalography
Electrocardiography
Learning systems
Health
Experiments

Keywords

  • Affective computing
  • ECG
  • EEG
  • EMG
  • Emotion recognition
  • Equine assisted therapy
  • Human-horse interaction
  • Physiological signals

Cite this

@article{5914ba89fb0443c08d9cd006d614748b,
title = "Examining human-horse interaction by means of affect recognition via physiological signals",
abstract = "For some time, equine assisted therapy (EAT), i.e. the use of horse-related activities for therapeutic reasons, has been recognised as a useful approach in the treatment of many mental health issues such as post-traumatic stress disorder (PTSD), depression, and anxiety. However, despite the interest in EAT, few scientific studies have focused on understanding the complex emotional response that horses seem to elicit in human riders and handlers. In this work, the potential use of affect recognition techniques based on physiological signals is examined for the task of assessing the interaction between humans and horses in terms of the emotional response of the humans to this interaction. Electroencephalography (EEG), electrocardiography (ECG), and electromyography (EMG) signals were captured from humans interacting with horses, and machine learning techniques were applied in order to predict the self-reported emotional states of the human subjects in terms of valence and arousal. Supervised classification experiments demonstrated the potential of this approach for affect recognition during human-horse interaction, reaching an F1-score of 78.27{\%} for valence and 65.49{\%} for arousal.",
keywords = "Affective computing, ECG, EEG, EMG, Emotion recognition, Equine assisted therapy, Human-horse interaction, Physiological signals",
author = "Turke Althobaiti and Stamos Katsigiannis and Daune West and Naeem Ramzan",
year = "2019",
month = "6",
day = "10",
doi = "10.1109/ACCESS.2019.2922037",
language = "English",
volume = "7",
pages = "77857--77867",
journal = "IEEE Access",
issn = "2169-3536",
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AU - Katsigiannis, Stamos

AU - West, Daune

AU - Ramzan, Naeem

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N2 - For some time, equine assisted therapy (EAT), i.e. the use of horse-related activities for therapeutic reasons, has been recognised as a useful approach in the treatment of many mental health issues such as post-traumatic stress disorder (PTSD), depression, and anxiety. However, despite the interest in EAT, few scientific studies have focused on understanding the complex emotional response that horses seem to elicit in human riders and handlers. In this work, the potential use of affect recognition techniques based on physiological signals is examined for the task of assessing the interaction between humans and horses in terms of the emotional response of the humans to this interaction. Electroencephalography (EEG), electrocardiography (ECG), and electromyography (EMG) signals were captured from humans interacting with horses, and machine learning techniques were applied in order to predict the self-reported emotional states of the human subjects in terms of valence and arousal. Supervised classification experiments demonstrated the potential of this approach for affect recognition during human-horse interaction, reaching an F1-score of 78.27% for valence and 65.49% for arousal.

AB - For some time, equine assisted therapy (EAT), i.e. the use of horse-related activities for therapeutic reasons, has been recognised as a useful approach in the treatment of many mental health issues such as post-traumatic stress disorder (PTSD), depression, and anxiety. However, despite the interest in EAT, few scientific studies have focused on understanding the complex emotional response that horses seem to elicit in human riders and handlers. In this work, the potential use of affect recognition techniques based on physiological signals is examined for the task of assessing the interaction between humans and horses in terms of the emotional response of the humans to this interaction. Electroencephalography (EEG), electrocardiography (ECG), and electromyography (EMG) signals were captured from humans interacting with horses, and machine learning techniques were applied in order to predict the self-reported emotional states of the human subjects in terms of valence and arousal. Supervised classification experiments demonstrated the potential of this approach for affect recognition during human-horse interaction, reaching an F1-score of 78.27% for valence and 65.49% for arousal.

KW - Affective computing

KW - ECG

KW - EEG

KW - EMG

KW - Emotion recognition

KW - Equine assisted therapy

KW - Human-horse interaction

KW - Physiological signals

U2 - 10.1109/ACCESS.2019.2922037

DO - 10.1109/ACCESS.2019.2922037

M3 - Article

VL - 7

SP - 77857

EP - 77867

JO - IEEE Access

JF - IEEE Access

SN - 2169-3536

ER -