Heart Rate Variability Signal Features for Emotion Recognition by Using Principal Component Analysis and Support Vectors Machine

Guo, Horng-Yuh; Huang, Yu-Shun; Lin, Chien-Hung; Chien, Jen-Chien; Haraikawa, Koichi; Shieh, Jiann-Shing

doi:10.1109/bibe.2016.40

Cited by 102 publications

(55 citation statements)

References 9 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: 76%

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: 76%

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

Nerin

Ferrandez

Fernández³

2019

Preprint

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“…Instead, they employ 'static' classifiers that process global features from the input time series (or for a small number of segments). Such approaches include Naive Bayes (NB) [40], [41], linear discriminant analysis (LDA) [42], and support vector machine (SVM) [43], [44], [45]. A summary can be found in Table I. A number of studies have sought to model temporal information within EEG signals, using hidden Markov models [46], Gaussian Process models [47], continuous conditional random fields [48], and long short-term memory (LSTM) neural networks [49].…”

Section: A Unimodal Heartbeat and Temporal Modelsmentioning

confidence: 99%

A Bayesian Deep Learning Framework for End-To-End Prediction of Emotion From Heartbeat

Harper

Southern

2022

IEEE Trans. Affective Comput.

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Automatic prediction of emotion promises to revolutionise human-computer interaction. Recent trends involve fusion of multiple modalities − audio, visual, and physiological − to classify emotional state. However, practical considerations 'in the wild' limit collection of this physiological data to commoditised heartbeat sensors. Furthermore, real-world applications often require some measure of uncertainty over model output. We present here an end-to-end deep learning model for classifying emotional valence from unimodal heartbeat data. We further propose a Bayesian framework for modelling uncertainty over valence predictions, and describe a procedure for tuning output according to varying demands on confidence. We benchmarked our framework against two established datasets within the field and achieved peak classification accuracy of 90%. These results lay the foundation for applications of affective computing in realworld domains such as healthcare, where a high premium is placed on non-invasive collection of data, and predictive certainty.

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“…Additionally, those studies that have explored unimodal heartbeat models for emotion detection tend to ignore temporal structures of the signal. Instead, they use 'static' classification methods that analyse global features of the input time-series, such as Naive Bayes (NB), [16], [18], linear discrimant analysis (LDA) [22], and support vector machine (SVM) [15], [19], [21]. A summary of these studies can be found in Table I.…”

Section: Related Workmentioning

confidence: 99%

End-To-End Prediction of Emotion from Heartbeat Data Collected by a Consumer Fitness Tracker

Harper

Southern

2019

2019 8th International Conference on Affective Computing and Intelligent Interaction (ACII)

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Automatic detection of emotion has the potential to revolutionize mental health and wellbeing. Recent work has been successful in predicting affect from unimodal electrocardiogram (ECG) data. However, to be immediately relevant for real-world applications, physiology-based emotion detection must make use of ubiquitous photoplethysmogram (PPG) data collected by affordable consumer fitness trackers. Additionally, applications of emotion detection in healthcare settings will require some measure of uncertainty over model predictions. We present here a Bayesian deep learning model for end-to-end classification of emotional valence, using only the unimodal heartbeat time series collected by a consumer fitness tracker (Garmin Vívosmart 3). We collected a new dataset for this task, and report a peak F1 score of 0.7. This demonstrates a practical relevance of physiology-based emotion detection 'in the wild' today.

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Heart Rate Variability Signal Features for Emotion Recognition by Using Principal Component Analysis and Support Vectors Machine

Cited by 102 publications

References 9 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

A Bayesian Deep Learning Framework for End-To-End Prediction of Emotion From Heartbeat

End-To-End Prediction of Emotion from Heartbeat Data Collected by a Consumer Fitness Tracker

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