Human emotion recognition using heart rate variability analysis with spectral bands based on respiration

Valderas, Maria Teresa; Bolea, Juan; Laguna, Pablo; Vallverdú, Montserrat; Bailón, Raquel

doi:10.1109/embc.2015.7319792

Cited by 52 publications

(41 citation statements)

References 11 publications

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“…However, if the PNS and SNS are linked with the positive/approach and negative/withdrawal responses [55], respectively, is more diffuse. As for HRV measures, both our results and those of other authors, showed differences in their response to either emotional dimensions [56], [20] and discrete emotions [57], [58], [21]. These differences are present in all levels of the HRV analysis, the time domain, the frequency domain and the Poincare plot.…”

Section: Discussionsupporting

confidence: 75%

The Psychological and Physiological Part of Emotions: Multimodal Approximation for Valence Classification

Nerin

Ferrandez

Fernández³

2019

Preprint

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In order to develop more precise and functional affective applications, it is necessary to achieve a balance between the psychology and the engineering applied to emotions. Signals from the central and peripheral nervous systems have been used for emotion recognition purposes, however, their operation and the relationship between them remains unknown. In this context, in the present work we have tried to approach the study of the psychobiology of both systems in order to generate a computational model for the recognition of emotions in the dimension of valence. To this end, the electroencephalography (EEG) signal, electrocardiography (ECG) signal and skin temperature of 24 subjects have been studied. Each methodology has been evaluated individually, finding characteristic patterns of positive and negative emotions in each of them. After feature selection of each methodology, the results of the classification showed that, although the classification of emotions is possible at both central and peripheral levels, the multimodal approach did not improve the results obtained through the EEG alone. In addition, differences have been observed between cerebral and physiological responses in the processing emotions by separating the sample by sex; though, the differences between men and women were only notable at the physiological level.

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Section: Discussionsupporting

confidence: 75%

The Psychological and Physiological Part of Emotions: Multimodal Approximation for Valence Classification

Nerin

Ferrandez

Fernández³

2019

Preprint

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“…It is debatable whether specific emotions can be robustly identified with this measure, but analysis of EEG spectral power can provide information on valence and arousal as well as on the strength of expressed emotions . A common issue when working with EEG is that there is no gold standard in the number of channels and their selection for analysis, nor in the data extraction and analysis methods . Practical issues, such as placing the electrodes, removing artifacts and noise (e.g., caused by muscle tension or movement), and real‐time recording and analysis may also limit the use of EEG‐based emotion expression in everyday practice.…”

Section: Different Aspects Of Emotional Expressionmentioning

confidence: 99%

“…It is important to highlight that systems using different sets of physiological parameters without facial expression are in many cases limited to the valence and arousal spectrum and fail to accurately identify specific emotions. Examples of such systems include combining: EEG with heart rate; heart rate variability with respiration; heart rate, skin conductance, and body temperature; and electrocardiogram (ECG), photoplethysmography, and galvanic skin response . Validation of these systems is still insufficient and there is very scarce data to reliably assess their clinical application.…”

Section: Different Aspects Of Emotional Expressionmentioning

confidence: 99%

Emotional expression in psychiatric conditions: New technology for clinicians

Grabowski

Rynkiewicz

Lassalle

et al. 2018

Psychiatry Clin Neurosci

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Aim Emotional expressions are one of the most widely studied topics in neuroscience, from both clinical and non‐clinical perspectives. Atypical emotional expressions are seen in various psychiatric conditions, including schizophrenia, depression, and autism spectrum conditions. Understanding the basics of emotional expressions and recognition can be crucial for diagnostic and therapeutic procedures. Emotions can be expressed in the face, gesture, posture, voice, and behavior and affect physiological parameters, such as the heart rate or body temperature. With modern technology, clinicians can use a variety of tools ranging from sophisticated laboratory equipment to smartphones and web cameras. The aim of this paper is to review the currently used tools using modern technology and discuss their usefulness as well as possible future directions in emotional expression research and treatment strategies. Methods The authors conducted a literature review in the PubMed, EBSCO, and SCOPUS databases, using the following key words: ‘emotions,’ ‘emotional expression,’ ‘affective computing,’ and ‘autism.’ The most relevant and up‐to‐date publications were identified and discussed. Search results were supplemented by the authors’ own research in the field of emotional expression. Results We present a critical review of the currently available technical diagnostic and therapeutic methods. The most important studies are summarized in a table. Conclusion Most of the currently available methods have not been adequately validated in clinical settings. They may be a great help in everyday practice; however, they need further testing. Future directions in this field include more virtual‐reality‐based and interactive interventions, as well as development and improvement of humanoid robots.

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“…Britton's research showed that the heart rate during a happy mood was lower than that in a neutral mood [7]. Valderas showed that the effects of relaxation and fear on heart rate were significantly different, and the average heart rate during happiness was lower than that while in a sad state [8]. Using the IBPSO algorithm, Xu et al collected ECG and heart rate signals for emotion recognition in which the highest recognition rate of sadness and joy was 92.10% [9].…”

Section: Introductionmentioning

confidence: 99%

Wearable Emotion Recognition Using Heart Rate Data from a Smart Bracelet

Shu

Chen

et al. 2020

Sensors

112

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Emotion recognition and monitoring based on commonly used wearable devices can play an important role in psychological health monitoring and human-computer interaction. However, the existing methods cannot rely on the common smart bracelets or watches for emotion monitoring in daily life. To address this issue, our study proposes a method for emotional recognition using heart rate data from a wearable smart bracelet. A ‘neutral + target’ pair emotion stimulation experimental paradigm was presented, and a dataset of heart rate from 25 subjects was established, where neutral plus target emotion (neutral, happy, and sad) stimulation video pairs from China’s standard Emotional Video Stimuli materials (CEVS) were applied to the recruited subjects. Normalized features from the data of target emotions normalized by the baseline data of neutral mood were adopted. Emotion recognition experiment results approved the effectiveness of ‘neutral + target’ video pair simulation experimental paradigm, the baseline setting using neutral mood data, and the normalized features, as well as the classifiers of Adaboost and GBDT on this dataset. This method will promote the development of wearable consumer electronic devices for monitoring human emotional moods.

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Human emotion recognition using heart rate variability analysis with spectral bands based on respiration

Cited by 52 publications

References 11 publications

The Psychological and Physiological Part of Emotions: Multimodal Approximation for Valence Classification

The Psychological and Physiological Part of Emotions: Multimodal Approximation for Valence Classification

Emotional expression in psychiatric conditions: New technology for clinicians

Wearable Emotion Recognition Using Heart Rate Data from a Smart Bracelet

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