Emotion recognition employing ECG and GSR signals as markers of ANS

Das, Priyanka; Khasnobish, Anwesha; Tibarewala, D. N.

doi:10.1109/casp.2016.7746134

Cited by 48 publications

(25 citation statements)

References 20 publications

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“…In the work of [ 58 ], the authors extracted the Welch’s PSD of ECG and Galvanic Skin response (GSR) signals and got a recognition rate of 91.62% by suing SVM. While in [ 101 ], features of EEG and eye signals were extracted by autoencoder.…”

Section: Methodsmentioning

confidence: 99%

“…The average verifying recognition rates of surprise, fear, disgust, grief, happy and angry respectively reached 78.72%, 73.37%, 70.48%, 62.65%, 62.52% and 44.93%. In [ 58 ], the author combined ECG and GSR signals to recognize emotions among happy, sad and neutral. The PSD features of ECG and GSR were extracted.…”

Section: Emotional Relevant Features Of Physiological Signalsmentioning

confidence: 99%

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A Review of Emotion Recognition Using Physiological Signals

Shu

Xie

Yang

et al. 2018

Sensors

625

352

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Emotion recognition based on physiological signals has been a hot topic and applied in many areas such as safe driving, health care and social security. In this paper, we present a comprehensive review on physiological signal-based emotion recognition, including emotion models, emotion elicitation methods, the published emotional physiological datasets, features, classifiers, and the whole framework for emotion recognition based on the physiological signals. A summary and comparation among the recent studies has been conducted, which reveals the current existing problems and the future work has been discussed.

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Section: Methodsmentioning

confidence: 99%

Section: Emotional Relevant Features Of Physiological Signalsmentioning

confidence: 99%

A Review of Emotion Recognition Using Physiological Signals

Shu

Xie

Yang

et al. 2018

Sensors

625

352

View full text Add to dashboard Cite

show abstract

“…Galvanic skin response (GSR) is physically the measure of change in the electrical properties of the skin [ 32 ] in response to changes in the ANS. Sympathetic activity causes increase in the sweat gland activity leading to a decrease in the level of skin resistance.…”

Section: Methodsmentioning

confidence: 99%

Emotion Recognition Based on Weighted Fusion Strategy of Multichannel Physiological Signals

Wei

Jia

Feng

et al. 2018

Computational Intelligence and Neuroscience

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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.

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“…The traditional method for monitoring heart rate measured the spikes of the potential generated by heart at each pressure pulse i.e. electrocardiogram, or ECG [9]. Physiological measurements, such as ECG signals, are robust because they are controlled by involuntary activations of the autonomic nervous system (ANS).…”

Section: Background and Related Workmentioning

confidence: 99%

“…Over the years, many studies have been conducted that utilized various physiological measures such as electroencephalogram (EEG) [3,4], skin conductance level or galvanic skin response (SCL/GSR) [5], electrocardiogram (ECG) [6], blood volume pressure (BVP) [7] or combination of two or more physiological markers [8,9] to monitor human well-being and state of mind. However, it was the heart rate variability, or heart rhythms that stood out as the most dynamic and reflective indicator of oneś emotional state and healthy well-being [10,11].…”

Section: Introductionmentioning

confidence: 99%