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 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.

scholarly journals A Globally Generalized Emotion Recognition System Involving Different Physiological Signals

Sensors ◽  
2018 ◽  
Vol 18 (6) ◽  
pp. 1905 ◽  
Author(s):  
Mouhannad Ali ◽  
Fadi Machot ◽  
Ahmad Mosa ◽  
Midhat Jdeed ◽  
Elyan Machot ◽  
...  
Keyword(s):  
Emotion Recognition ◽  
Recognition System ◽  
Physiological Signals

Physiological signal-based emotion recognition system

2017 ◽  
Author(s):  
Saif Hassani ◽  
Ibrahim Bafadel ◽  
Abdelrahman Bekhatro ◽  
Ebraheim Al Blooshi ◽  
Soha Ahmed ◽  
...  
Keyword(s):  
Emotion Recognition ◽  
Recognition System ◽  
Physiological Signal

Human Physiological Signal Recognition Based on Piezoelectric Impedance Technique

2014 ◽  
Vol 687-691 ◽  
pp. 4089-4092
Author(s):  
Jun Zhang ◽  
Jie Huang ◽  
Hong Mei Tang ◽  
Xian Hua Li ◽  
Qing Yang Cai
Keyword(s):  
Human Body ◽  
Physiological Signals ◽  
Signal Recognition ◽  
Coupling Impedance ◽  
Experiment Platform ◽  
Physiological Signal ◽  
Impedance Technique ◽  
Piezoelectric Coupling ◽  
Piezoelectric Impedance ◽  
Set Up

In order to verify whether the piezoelectric impedance technology can be applied to detect the physiological signals of human body, the principle of piezoelectric coupling impedance theory and piezoelectric impedance technology using for human physiological signal detection was introduced in this paper. With an experiment platform set up, detection experiments based on the piezoelectric impedance technology were created. And the experimental1 was improved to avoid the influence of man-made factors on experiment result. Two methods were used to deal with the experimental data. The results show that the piezoelectric impedance technique can be applied to identify the human body physiological signal, and offers a totally new idea to detect the physiological signals of human body.

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.

scholarly journals Real-time emotion recognition system with multiple physiological signals

2019 ◽  
Vol 13 (4) ◽  
pp. JAMDSM0075-JAMDSM0075 ◽  
Author(s):  
Jyun-Rong ZHUANG ◽  
Ya-Jing GUAN ◽  
Hayato NAGAYOSHI ◽  
Keiichi MURAMATSU ◽  
Keiichi WATANUKI ◽  
...  
Keyword(s):  
Emotion Recognition ◽  
Real Time ◽  
Recognition System ◽  
Physiological Signals

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.

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