ECG-Based Human Emotion Recognition Across Multiple Subjects

Nikolova, Desislava; Mihaylova, Petia; Manolova, Agata; Georgieva, Pétia

doi:10.1007/978-3-030-23976-3_3

Cited by 13 publications

(9 citation statements)

References 5 publications

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“…Системи розпізнавання емоцій мають багато потенційних застосувань, охоплюючи охорону здоров'я, розваги, електронне навчання, маркетинг, моніторинг людей і безпеку. Згідно з [3], існувало три основних застосування систем розпізнавання емоцій конкретно за допомогою сигналів ЕКГ:…”

Section: постановка проблеми у загальному вигляді та її зв'язок із ва...unclassified

Стратегії Злиття Фізіологічних Сигналів Для Розпізнавання Емоцій

СУХОЛЕЙСТЕР,

НАКОНЕЧНИЙ

2023

ВОТТП

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Дана стаття зосереджена на аналізі та класифікації стратегій розпізнавання емоцій на основі фізіологічних сигналів, а також на дослідженні доступних баз даних для мультимодального аналізу психологічних станів з використанням інформації фізіологічних сигналів. У статті представлено класифікацію методів розпізнавання, проведено аналіз впливів факторів модальності і стратегії злиття на результати розпізнавання та визначено важливість вибору фізіологічних сигналів для точного розпізнавання емоцій. Також досліджено переваги моделей глибокого навчання та практичні застосування розпізнавання емоцій у реальних сценаріях.

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Section: постановка проблеми у загальному вигляді та її зв'язок із ва...unclassified

Стратегії Злиття Фізіологічних Сигналів Для Розпізнавання Емоцій

СУХОЛЕЙСТЕР,

НАКОНЕЧНИЙ

2023

ВОТТП

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“…Physiological data used for this purpose are electrocardiography (ECG), heart rate variability, electroencephalography (EEG), facial recognition, forehead biosignal, speech recognition, skin temperature (SKT), blood volume pulse, and respiration (RSP). [ 6 7 8 9 10 ] In Zhang et al .’s study,[ 11 ] RSP signals were studied to recognize emotions. The model was developed using the DEAP dataset[ 12 ] and the Augsburg University dataset.…”

Section: Introductionmentioning

confidence: 99%

An Emotion Recognition Embedded System using a Lightweight Deep Learning Model

Bazargani,

Tahmasebi,

Yazdchi

et al. 2023

Journal of Medical Signals &Amp; Sensors

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Background: Diagnosing emotional states would improve human-computer interaction (HCI) systems to be more effective in practice. Correlations between Electroencephalography (EEG) signals and emotions have been shown in various research; therefore, EEG signal-based methods are the most accurate and informative. Methods: In this study, three Convolutional Neural Network (CNN) models, EEGNet, ShallowConvNet and DeepConvNet, which are appropriate for processing EEG signals, are applied to diagnose emotions. We use baseline removal preprocessing to improve classification accuracy. Each network is assessed in two setting ways: subject-dependent and subject-independent. We improve the selected CNN model to be lightweight and implementable on a Raspberry Pi processor. The emotional states are recognized for every three-second epoch of received signals on the embedded system, which can be applied in real-time usage in practice. Results: Average classification accuracies of 99.10% in the valence and 99.20% in the arousal for subject-dependent and 90.76% in the valence and 90.94% in the arousal for subject independent were achieved on the well-known DEAP dataset. Conclusion: Comparison of the results with the related works shows that a highly accurate and implementable model has been achieved for practice.

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“…Hardware methods for obtaining information about a person’s emotions can be classified into two groups [ 4 ]. The methods of the first group are based on physiological parameters [ 5 ] obtained on the basis of electroencephalogram (EEG) [ 6 , 7 , 8 ], electrocardiogram (ECG) [ 9 , 10 ], and electromyogram (EMG) [ 11 , 12 ]. Emotions can also be assessed based on temperature [ 13 , 14 ], skin-galvanic reaction, breathing patterns, heart rate [ 15 , 16 , 17 ], etc.…”

Section: Introductionmentioning

confidence: 99%

Assessment of a Person’s Emotional State Based on His or Her Posture Parameters

Shichkina,

Bureneva,

Salaurov

et al. 2023

Sensors

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This article is devoted to the study of the correlation between the emotional state of a person and the posture of his or her body in the sitting position. In order to carry out the study, we developed the first version of the hardware-software system based on a posturometric armchair, allowing the characteristics of the posture of a sitting person to be evaluated using strain gauges. Using this system, we revealed the correlation between sensor readings and human emotional states. We showed that certain readings of a sensor group are formed for a certain emotional state of a person. We also found that the groups of triggered sensors, their composition, their number, and their location are related to the states of a particular person, which led to the need to build personalized digital pose models for each person. The intellectual component of our hardware–software complex is based on the concept of co-evolutionary hybrid intelligence. The system can be used during medical diagnostic procedures and rehabilitation processes, as well as in controlling people whose professional activity is connected with increased psycho-emotional load and can cause cognitive disorders, fatigue, and professional burnout and can lead to the development of diseases.

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ECG-Based Human Emotion Recognition Across Multiple Subjects

Cited by 13 publications

References 5 publications

Стратегії Злиття Фізіологічних Сигналів Для Розпізнавання Емоцій

Стратегії Злиття Фізіологічних Сигналів Для Розпізнавання Емоцій

An Emotion Recognition Embedded System using a Lightweight Deep Learning Model

Assessment of a Person’s Emotional State Based on His or Her Posture Parameters

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