The Research on Emotion Recognition from ECG Signal

Cai, Jing; Liu, Guangyuan; Hao, Min

doi:10.1109/itcs.2009.108

Cited by 56 publications

(22 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For the recognition of emotions, in most cases, QRS Complex is used, which defines activation of the heart related with human emotional state and is a suitable indicator to recognize main emotions, but there are also difficulties in the emotion recognition due to the fact that this indicator has variant sensitivity to specific emotions. Results of research provided by Cai, Liu, and Hao [73], shows that sadness can be recognized more easily and precisely than emotion of joy. The majority of studies related to ECG based emotion recognition focus on the definition and evaluation of QRS amplitudes and the duration between those waves.…”

Section: Electrocardiography (Ecg)mentioning

confidence: 99%

Human Emotion Recognition: Review of Sensors and Methods

Dzedzickis

Kaklauskas

Bučinskas

2020

Sensors

408

170

View full text Add to dashboard Cite

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.

show abstract

Section: Electrocardiography (Ecg)mentioning

confidence: 99%

Human Emotion Recognition: Review of Sensors and Methods

Dzedzickis

Kaklauskas

Bučinskas

2020

Sensors

408

170

View full text Add to dashboard Cite

show abstract

“…Nonlinear analysis can be proposed to find the best relevant emotional features and relate them to emotional states. Jing et al [16] pointed out that the introduction of emotions based on physiological signals is an important field of research. This study was first performed with conditions of joy and sadness.…”

Section: Literature Studymentioning

confidence: 99%

Image Extraction of ECG Signals on Human Emotions Using Wavelet Transformation

Arifin¹,

Amalia²,

Maulana³

2018

Proceedings of the the 1st International Conference on Computer Science and Engineering Technology Universitas Muria Kudus

View full text Add to dashboard Cite

Data processing of human heart's signals can be learned and applied in several studies, whether in psychological subject or in monitoring the condition of the human heart. A human's emotional aspect could be observed when the person recorded his heartbeat activities. The state of emotion observed in a normal, happy and sad conditions.The first stage of this research consists of measuring, recording, printing out the ECG data and scanning.The second stage, which named as pregeneration of the ECG image data stage, consists of median filtering and determining the threshold value and morphology. The third stage uses thinning and spatial-to-time methods for processing its ECG image.The last stage is named as characteristic extraction which uses a wavelet transform. From the characteristic extraction, the average of energy signal is obtained.

show abstract

“…ECG signal analysis is widely used in medicine, since it offers information regarding diseases and disorders affecting the cardiac function. Several studies [3][4][5][6][7] conclude that emotions clearly affect the cardiac activity.…”

Section: Introductionmentioning

confidence: 99%

A Wrapper Feature Selection Algorithm: An Emotional Assessment Using Physiological Recordings from Wearable Sensors

Mohíno-Herranz

Gil-Pita

García-Gómez

et al. 2020

Sensors

View full text Add to dashboard Cite

Assessing emotional state is an emerging application field boosting research activities on the topic of analysis of non-invasive biosignals to find effective markers to accurately determine the emotional state in real-time. Nowadays using wearable sensors, electrocardiogram and thoracic impedance measurements can be recorded, facilitating analyzing cardiac and respiratory functions directly and autonomic nervous system function indirectly. Such analysis allows distinguishing between different emotional states: neutral, sadness, and disgust. This work was specifically focused on the proposal of a k-fold approach for selecting features while training the classifier that reduces the loss of generalization. The performance of the proposed algorithm used as the selection criterion was compared to the commonly used standard error function. The proposed k-fold approach outperforms the conventional method with 4% hit success rate improvement, reaching an accuracy near to 78%. Moreover, the proposed selection criterion method allows the classifier to produce the best performance using a lower number of features at lower computational cost. A reduced number of features reduces the risk of overfitting while a lower computational cost contributes to implementing real-time systems using wearable electronics.

show abstract

The Research on Emotion Recognition from ECG Signal

Cited by 56 publications

References 4 publications

Human Emotion Recognition: Review of Sensors and Methods

Human Emotion Recognition: Review of Sensors and Methods

Image Extraction of ECG Signals on Human Emotions Using Wavelet Transformation

A Wrapper Feature Selection Algorithm: An Emotional Assessment Using Physiological Recordings from Wearable Sensors

Contact Info

Product

Resources

About