scholarly journals Emotion recognition techniques using physiological signals and video games –Systematic review–

2017 ◽  
Vol 26 (46) ◽  
Author(s):  
Mauro Callejas-Cuervo ◽  
Laura Alejandra Martínez-Tejada ◽  
Andrea Catherine Alarcón-Aldana
Keyword(s):  
Systematic Review ◽  
Video Games ◽  
Emotion Recognition ◽  
Emotional Reactions ◽  
Physiological Signals ◽  
Multimedia Tools ◽  
Physiological Signal ◽  
Important Approach ◽  
The Individual ◽  
Education Psychology

Emotion recognition systems from physiological signals are innovative techniques that allow studying the behavior and reaction of an individual when exposed to information that may evoke emotional reactions through multimedia tools, for example, video games. This type of approach is used to identify the behavior of an individual in different fields, such as medicine, education, psychology, etc., in order to assess the effect that the content has on the individual that is interacting with it. This article shows a systematic review of articles that report studies on emotion recognition with physiological signals and video games, between January 2010 and April 2016. We searched in eight databases, and found 15 articles that met the selection criteria. With this systematic review, we found that the use of video games as emotion stimulation tools has become an innovative field of study, due to their potential to involve stories and multimedia tools that can interact directly with the person in fields like rehabilitation. We detected clear examples where video games and physiological signal measurement became an important approach in rehabilitation processes, for example, in Posttraumatic Stress Disorder (PTSD) treatments.

scholarly journals Design of User-Customized Negative Emotion Classifier Based on Feature Selection Using Physiological Signal Sensors

Sensors ◽  
2018 ◽  
Vol 18 (12) ◽  
pp. 4253 ◽  
Author(s):  
JeeEun Lee ◽  
Sun K. Yoo
Keyword(s):  
Feature Selection ◽  
Negative Emotion ◽  
Electrodermal Activity ◽  
Cognitive Status ◽  
Physiological Signals ◽  
Self Assessment ◽  
Physiological Signal ◽  
Distribution Shape ◽  
The Difference ◽  
The Individual

First, the Likert scale and self-assessment manikin are used to provide emotion analogies, but they have limits for reflecting subjective factors. To solve this problem, we use physiological signals that show objective responses from cognitive status. The physiological signals used are electrocardiogram, skin temperature, and electrodermal activity (EDA). Second, the degree of emotion felt, and the related physiological signals, vary according to the individual. KLD calculates the difference in probability distribution shape patterns between two classes. Therefore, it is possible to analyze the relationship between physiological signals and emotion. As the result, features from EDA are important for distinguishing negative emotion in all subjects. In addition, the proposed feature selection algorithm showed an average accuracy of 92.5% and made it possible to improve the accuracy of negative emotion recognition.

Music mood and human emotion recognition based on physiological signals: a systematic review

2021 ◽  
Author(s):  
Vybhav Chaturvedi ◽  
Arman Beer Kaur ◽  
Vedansh Varshney ◽  
Anupam Garg ◽  
Gurpal Singh Chhabra ◽  
...  
Keyword(s):  
Systematic Review ◽  
Emotion Recognition ◽  
Physiological Signals ◽  
Human Emotion

scholarly journals An Emotion Recognition Based on Physiological Signals

2019 ◽  
Vol 8 (9S) ◽  
pp. 335-341
Keyword(s):  
Emotion Recognition ◽  
Pulse Signal ◽  
Physiological Signals ◽  
Computer Interface ◽  
Human Computer Interface ◽  
Emotion Detection ◽  
Extraction Techniques ◽  
Emotion Elicitation ◽  
Physiological Signal ◽  
Plausible Mechanism

Emotion recognition is alluring considerable interest among the researchers. Emotions are discovered by facial, speech, gesture, posture and physiological signals. Physiological signals are a plausible mechanism to recognize emotion using human-computer interaction. The objective of this paper is to put forth the recognition of emotions using physiological signals. Various emotion elicitation protocols, feature extraction techniques, classification methods that aim at recognizing emotions from physiological signals are discussed here. Wrist Pulse Signal is also discussed to fill the lacunae of the other physiological signal for emotion detection. Working on basic as well as non-basic human emotion and human-computer interface will make the system robust.

Emotion Recognition Based on Chaos Characteristics of Physiological Signals

2013 ◽  
Vol 380-384 ◽  
pp. 3750-3753 ◽  
Author(s):  
Chun Yan Nie ◽  
Rui Li ◽  
Ju Wang
Keyword(s):  
Nonlinear Dynamics ◽  
Emotion Recognition ◽  
Classical Theory ◽  
Chaos Theory ◽  
Biomedical Engineering ◽  
Recognition System ◽  
Physiological Signals ◽  
Signal Recognition ◽  
Physiological Signal ◽  
Linear Signal

Changes of physiological signals are affected by human emotions, but also the emotional fluctuations are reflected by the body's variation of physiological signal's feature. Physiological signal is a non-linear signal ,nonlinear dynamics and biomedical engineering ,which based on chaos theory, providing us a new method for studying on the parameters of these complex physiological signals which can hardly described by the classical theory. This paper shows physiological emotion signal recognition system based on the chaotic characteristics, and than describes some current applications of chaotic characteristics for multiple physiological signals on emotional recognition.

scholarly journals The Design of CNN Architectures for Optimal Six Basic Emotion Classification Using Multiple Physiological Signals

Sensors ◽  
2020 ◽  
Vol 20 (3) ◽  
pp. 866 ◽  
Author(s):  
SeungJun Oh ◽  
Jun-Young Lee ◽  
Dong Keun Kim
Keyword(s):  
Emotion Recognition ◽  
Identification Accuracy ◽  
Physiological Signals ◽  
Dominant Factor ◽  
Basic Emotion ◽  
Emotion Classification ◽  
Physiological Signal ◽  
Proposed Model ◽  
Respiration Sensor ◽  
Signal Combination

This study aimed to design an optimal emotion recognition method using multiple physiological signal parameters acquired by bio-signal sensors for improving the accuracy of classifying individual emotional responses. Multiple physiological signals such as respiration (RSP) and heart rate variability (HRV) were acquired in an experiment from 53 participants when six basic emotion states were induced. Two RSP parameters were acquired from a chest-band respiration sensor, and five HRV parameters were acquired from a finger-clip blood volume pulse (BVP) sensor. A newly designed deep-learning model based on a convolutional neural network (CNN) was adopted for detecting the identification accuracy of individual emotions. Additionally, the signal combination of the acquired parameters was proposed to obtain high classification accuracy. Furthermore, a dominant factor influencing the accuracy was found by comparing the relativeness of the parameters, providing a basis for supporting the results of emotion classification. The users of this proposed model will soon be able to improve the emotion recognition model further based on CNN using multimodal physiological signals and their sensors.

Emotion recognition from physiological signals and video games to detect personality traits

2020 ◽  
pp. 1-9
Author(s):  
M. Callejas-Cuervo ◽  
L.A. Martínez-Tejada ◽  
A.C. Alarcón-Aldana
Keyword(s):  
Emotion Recognition ◽  
Galvanic Skin Response ◽  
Skin Conductance Response ◽  
Statistical Data ◽  
Inverse Correlation ◽  
Physiological Signals ◽  
Physiological Signal ◽  
First Approximation ◽  
Skin Response ◽  
Electrocardiogram Ecg

This paper presents a system that allows for the identification of two values: arousal and valence, which represent the degree of stimulation in a subject, using Russell’s model of affect as a reference. To identify emotions, a step-by-step structure is used, which, based on statistical data from physiological signal metrics, generates the representative arousal value (direct correlation); from the PANAS questionnaire, the system generates the valence value (inverse correlation), as a first approximation to the techniques of emotion recognition without the use of artificial intelligence. The system gathers information concerning arousal activity from a subject using the following metrics: beats per minute (BPM), heart rate variability (HRV), the number of galvanic skin response (GSR) peaks in the skin conductance response (SCR) and forearm contraction time, using three physiological signals (Electrocardiogram - ECG, Galvanic Skin Response - GSR, Electromyography - EMG).

Application of Chaos Characteristics about Physiological Signals in Emotion Recognition Based on Approximate Entropy

2014 ◽  
Vol 543-547 ◽  
pp. 2539-2542
Author(s):  
Chun Yan Nie ◽  
Hai Xin Sun ◽  
Ju Wang
Keyword(s):  
Emotion Recognition ◽  
Frequency Domain ◽  
Affective Computing ◽  
System Modeling ◽  
Approximate Entropy ◽  
Physiological Signals ◽  
Ecg Signal ◽  
Human Beings ◽  
Physiological Signal ◽  
Study Results

Emotion recognition is an important part in affective computing. It is the basis of building a harmonious man-machine environment. Respiratory (RSP) signal and electrocardiogram (ECG) signal are one of the main study objects in the emotion recognition based on physiological signal. The variations of the RSP signal and the ECG signal is one of the true performances of the human emotions. Through the analyses of the RSP signal and the ECG signal, we can recognize the inner emotion variations of human beings. This lays the foundation for the system modeling of emotion recognition. In this paper, we study the approximate entropy extraction of the physiological signals and analyze the chaotic characteristics and frequency domain characteristics of the approximate entropy under different emotions. The study results show that the different emotion status is corresponding to different approximate entropy and different variations in the frequency domain.

scholarly journals Affective computing in the context of music therapy: a systematic review

2021 ◽  
Vol 10 (15) ◽  
pp. e392101522844
Author(s):  
Maíra Araújo de Santana ◽  
Clarisse Lins de Lima ◽  
Arianne Sarmento Torcate ◽  
Flávio Secco Fonseca ◽  
Wellington Pinheiro dos Santos
Keyword(s):  
Artificial Intelligence ◽  
Systematic Review ◽  
Music Therapy ◽  
Emotion Recognition ◽  
Affective Computing ◽  
Intelligent Computing ◽  
Important Approach ◽  
Review Protocol ◽  
Automatic Search ◽  
Recognition Of Emotions

Music therapy is an effective tool to slow down the progress of dementia since interaction with music may evoke emotions that stimulates brain areas responsible for memory. This therapy is most successful when therapists provide adequate and personalized stimuli for each patient. This personalization is often hard. Thus, Artificial Intelligence (AI) methods may help in this task. This paper brings a systematic review of the literature in the field of affective computing in the context of music therapy. We particularly aim to assess AI methods to perform automatic emotion recognition applied to Human-Machine Musical Interfaces (HMMI). To perform the review, we conducted an automatic search in five of the main scientific databases on the fields of intelligent computing, engineering, and medicine. We search all papers released from 2016 and 2020, whose metadata, title or abstract contains the terms defined in the search string. The systematic review protocol resulted in the inclusion of 144 works from the 290 publications returned from the search. Through this review of the state-of-the-art, it was possible to list the current challenges in the automatic recognition of emotions. It was also possible to realize the potential of automatic emotion recognition to build non-invasive assistive solutions based on human-machine musical interfaces, as well as the artificial intelligence techniques in use in emotion recognition from multimodality data. Thus, machine learning for recognition of emotions from different data sources can be an important approach to optimize the clinical goals to be achieved through music therapy.

scholarly journals Emotion Recognition Based on Weighted Fusion Strategy of Multichannel Physiological Signals

2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Wei Wei ◽  
Qingxuan Jia ◽  
Yongli Feng ◽  
Gang Chen
Keyword(s):  
Emotion Recognition ◽  
Recognition Rate ◽  
Recognition System ◽  
Physiological Signals ◽  
Support Vector ◽  
Svm Classifier ◽  
Fusion Strategy ◽  
Pattern Recognition Problem ◽  
Physiological Signal ◽  
Decision Level

Emotion recognition is an important pattern recognition problem that has inspired researchers for several areas. Various data from humans for emotion recognition have been developed, including visual, audio, and physiological signals data. This paper proposes a decision-level weight fusion strategy for emotion recognition in multichannel physiological signals. Firstly, we selected four kinds of physiological signals, including Electroencephalography (EEG), Electrocardiogram (ECG), Respiration Amplitude (RA), and Galvanic Skin Response (GSR). And various analysis domains have been used in physiological emotion features extraction. Secondly, we adopt feedback strategy for weight definition, according to recognition rate of each emotion of each physiological signal based on Support Vector Machine (SVM) classifier independently. Finally, we introduce weight in decision level by linear fusing weight matrix with classification result of each SVM classifier. The experiments on the MAHNOB-HCI database show the highest accuracy. The results also provide evidence and suggest a way for further developing a more specialized emotion recognition system based on multichannel data using weight fusion strategy.

The importance and significance of gene polymorphisms in preeclampsia

2016 ◽  
pp. 45-49
Author(s):  
P.N. Veropotvelyan ◽  
◽  
I.S. Tsehmistrenko ◽  
N.P. Veropotvelyan ◽  
N.S. Rusak ◽  
...  
Keyword(s):  
Oxidative Stress ◽  
Systematic Review ◽  
Early Diagnosis ◽  
Gene Polymorphisms ◽  
Individual Risk ◽  
Extended Study ◽  
Stress Genes ◽  
Detoxification System ◽  
The Individual ◽  
The Relationship

Was to conduct a systematic review of data on the relationship between polymorphisms genes of detoxification system and development of preeclampsia (РЕ). Рresents the main genes of detoxification system (GSTPI, GSTМI, GSTТI, GРХI, ЕРНХI, SOD-2, SOD-3, CYPIAL, MTHЕR, MTR) and their functions. Of interest is the possibility of calculating the individual risk of PE based on the results about the presence of a combination of different polymorphisms in the genotype of the female. Question about early diagnosis of РЕ remains controversial and not fully understood. It is necessary to conduct further in-depth, extended study of this problem. Key words: preeclampsia, oxidative stress, genes of the detoxification system.

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