Heart sound signals can be used for emotion recognition

Cheng, Xiefeng; Wang, Yue; Dai, Shicheng; Zhao, Pei; Liu, Qifa

doi:10.1038/s41598-019-42826-2

Cited by 44 publications

(32 citation statements)

References 13 publications

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“…Compared to EEG or ECG, HRV (especially based on the PPG technique) is a more comfortable, cheap, and quite universal method. A variety of possible measurement methods in [110] presented a HRV evaluation approach based on heart sound measurements, proving that heart sound correlates well with RR interval from ECG. Another HVR measurement approach using Doppler radar presented by Boris-Lubecke et.…”

Section: Heart Rate Variability (Hrv)mentioning

confidence: 99%

Human Emotion Recognition: Review of Sensors and Methods

Dzedzickis

Kaklauskas

Bučinskas

2020

Sensors

408

170

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Automated emotion recognition (AEE) is an important issue in various fields of activities which use human emotional reactions as a signal for marketing, technical equipment, or human–robot interaction. This paper analyzes scientific research and technical papers for sensor use analysis, among various methods implemented or researched. This paper covers a few classes of sensors, using contactless methods as well as contact and skin-penetrating electrodes for human emotion detection and the measurement of their intensity. The results of the analysis performed in this paper present applicable methods for each type of emotion and their intensity and propose their classification. The classification of emotion sensors is presented to reveal area of application and expected outcomes from each method, as well as their limitations. This paper should be relevant for researchers using human emotion evaluation and analysis, when there is a need to choose a proper method for their purposes or to find alternative decisions. Based on the analyzed human emotion recognition sensors and methods, we developed some practical applications for humanizing the Internet of Things (IoT) and affective computing systems.

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Section: Heart Rate Variability (Hrv)mentioning

confidence: 99%

Human Emotion Recognition: Review of Sensors and Methods

Dzedzickis

Kaklauskas

Bučinskas

2020

Sensors

408

170

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“…More uncommon but revealing emotional responses including neural changes revealed by neuroimaging ( Critchley and Harrison, 2013 ), genetic changes ( Jonassen and Landrø, 2014 ), and inflammation ( Pace-Schott et al, 2019 ) could be implemented into future emotion recognition technologies. Heart sounds can even be used in emotion recognition ( Xiefeng et al, 2019 ). Heart rate variability (HRV) is the tiny variations in sinus heartbeats ( Shi et al, 2017 ).…”

Section: Other Responses To Emotionmentioning

confidence: 99%

“…Emotion recognition technologies may provide a basis for monitoring emotional health and could even be used to monitor for emotion-related mental health disorders (Xiefeng et al, 2019). Because the physiological changes expressed by emotions are not voluntarily controlled, they may provide a more accurate reflection of the true emotional experiences people may be having (Wu et al, 2012).…”

Section: Introductionmentioning

confidence: 99%

Respiratory Rhythm, Autonomic Modulation, and the Spectrum of Emotions: The Future of Emotion Recognition and Modulation

Jerath¹,

Beveridge²

2020

Front. Psychol.

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Pulmonary ventilation and respiration are considered to be primarily involved in oxygenation of blood for oxygen delivery to cells throughout the body for metabolic purposes. Other pulmonary physiological observations, such as respiratory sinus arrhythmia, Hering Brewer reflex, cardiorespiratory synchronization, and the heart rate variability (HRV) relationship with breathing rhythm, lack complete explanations of physiological/functional significance. The spectrum of waveforms of breathing activity correlate to anxiety, depression, anger, stress, and other positive and negative emotions. Respiratory pattern has been thought not only to be influenced by emotion but to itself influence emotion in a bi-directional relationship between the body and the mind. In order to show how filling in gaps in understanding could lead to certain future developments in mind-body medicine, biofeedback, and personal health monitoring, we review and discuss empirical work and tracings to express the vital role of bodily rhythms in influencing emotion, autonomic nervous system activity, and even general neural activity. Future developments in measurement and psychophysiological understanding of the pattern of breathing in combination with other parameters such as HRV, cardiorespiratory synchronization, and skin conductivity may allow for biometric monitoring systems to one day accurately predict affective state and even affective disorders such as anxiety. Better affective prediction based on recent research when incorporated into personal health monitoring devices could greatly improve public mental health by providing at-home biofeedback for greater understanding of one's mental state and for mind-body affective treatments such as breathing exercises.

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“…The multiple-configuration Butterworth filter is the most widely used filter based on the summarized papers. In [47,74], a low-pass Butterworth filter with a 60 Hz cut-off frequency was applied to remove a higher background noise of ECG signals. A 0.05-100 Hz Butterworth bandpass filter was used in [69] to remove noise, while a 49-51 Hz band-stop Butterworth filter was used in [75] for power line interference at 50 Hz.…”

Section: Pre-processingmentioning

confidence: 99%

Electrocardiogram-Based Emotion Recognition Systems and Their Applications in Healthcare—A Review

Hasnul

Aziz

Alelyani

et al. 2021

Sensors

118

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Affective computing is a field of study that integrates human affects and emotions with artificial intelligence into systems or devices. A system or device with affective computing is beneficial for the mental health and wellbeing of individuals that are stressed, anguished, or depressed. Emotion recognition systems are an important technology that enables affective computing. Currently, there are a lot of ways to build an emotion recognition system using various techniques and algorithms. This review paper focuses on emotion recognition research that adopted electrocardiograms (ECGs) as a unimodal approach as well as part of a multimodal approach for emotion recognition systems. Critical observations of data collection, pre-processing, feature extraction, feature selection and dimensionality reduction, classification, and validation are conducted. This paper also highlights the architectures with accuracy of above 90%. The available ECG-inclusive affective databases are also reviewed, and a popularity analysis is presented. Additionally, the benefit of emotion recognition systems towards healthcare systems is also reviewed here. Based on the literature reviewed, a thorough discussion on the subject matter and future works is suggested and concluded. The findings presented here are beneficial for prospective researchers to look into the summary of previous works conducted in the field of ECG-based emotion recognition systems, and for identifying gaps in the area, as well as in developing and designing future applications of emotion recognition systems, especially in improving healthcare.

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Heart sound signals can be used for emotion recognition

Cited by 44 publications

References 13 publications

Human Emotion Recognition: Review of Sensors and Methods

Human Emotion Recognition: Review of Sensors and Methods

Respiratory Rhythm, Autonomic Modulation, and the Spectrum of Emotions: The Future of Emotion Recognition and Modulation

Electrocardiogram-Based Emotion Recognition Systems and Their Applications in Healthcare—A Review

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