Remote heart rate variability for emotional state monitoring
Several researches have been conducted to recognize emotions using various modalities such as facial expressions, gestures, speech or physiological signals. Among all these modalities, physiological signals are especially interesting because they are mainly controlled by the autonomic nervous system...
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Published in | 2018 IEEE EMBS International Conference on Biomedical & Health Informatics (BHI) pp. 153 - 156 |
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Main Authors | , , , , , |
Format | Conference Proceeding |
Language | English |
Published |
IEEE
01.03.2018
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Abstract | Several researches have been conducted to recognize emotions using various modalities such as facial expressions, gestures, speech or physiological signals. Among all these modalities, physiological signals are especially interesting because they are mainly controlled by the autonomic nervous system. It has been shown for example that there is an undeniable relationship between emotional state and Heart Rate Variability (HRV). In this paper, we present a methodology to monitor emotional state from physiological signals acquired remotely. The method is based on a remote photoplethysmography (rPPG) algorithm that estimates remote Heart Rate Variability (rHRV) using a simple camera. We first show that the rHRV signal can be estimated with a high accuracy (more than 96% in frequency domain). Then, frequency-feature of rHRV is calculated and we show that there is a strong correlation between the rHRV feature and different emotional states. This observation has been validated on 12 out of 16 volunteers and video-induced emotions which opens the way to contactless monitoring of emotions from physiological signals. |
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AbstractList | Several researches have been conducted to recognize emotions using various modalities such as facial expressions, gestures, speech or physiological signals. Among all these modalities, physiological signals are especially interesting because they are mainly controlled by the autonomic nervous system. It has been shown for example that there is an undeniable relationship between emotional state and Heart Rate Variability (HRV). In this paper, we present a methodology to monitor emotional state from physiological signals acquired remotely. The method is based on a remote photoplethysmography (rPPG) algorithm that estimates remote Heart Rate Variability (rHRV) using a simple camera. We first show that the rHRV signal can be estimated with a high accuracy (more than 96% in frequency domain). Then, frequency-feature of rHRV is calculated and we show that there is a strong correlation between the rHRV feature and different emotional states. This observation has been validated on 12 out of 16 volunteers and video-induced emotions which opens the way to contactless monitoring of emotions from physiological signals. |
Author | Macwan, Richard Nakamura, Keisuke Li, Peixi Benezeth, Yannick Yang, Fan Gomez, Randy |
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Snippet | Several researches have been conducted to recognize emotions using various modalities such as facial expressions, gestures, speech or physiological signals.... |
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SubjectTerms | Biomedical monitoring Cameras Emotion recognition Heart rate variability Monitoring Resonant frequency Sensors |
Title | Remote heart rate variability for emotional state monitoring |
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