GSR Signals Features Extraction for Emotion Recognition

Kipli, Kuryati; Latip, Aisya Amelia Abdul; Lias, Kasumawati; Bateni, Norazlina; Yusoff, Salmah Mohamad; Tajudin, Nurul Mirza Afiqah; Jalil, M. A.; Ray, Kanad; Kaiser, M. Shamim; Mahmud, Murni

doi:10.1007/978-981-16-8826-3_28

Cited by 3 publications

(1 citation statement)

References 27 publications

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“…Thus, in this study, to recognize the emotion of the subject, physiological signals namely, EEG and ECG are considered. Although in the literature, some other physiological signals are also used to determine the emotional state of a person, such as changes in the conductivity of the skin, which is measured using GSR [ 9 , 10 , 11 ]. In [ 12 ], the authors have extracted time–frequency domain features using fractional Fourier transform (FrFT); the most relevant features are selected using the Wilcoxon method, which is then given as input to a support vector machine (SVM) classifier for determining the emotional state of the person.…”

Section: Introductionmentioning

confidence: 99%

Automated Emotion Identification Using Fourier–Bessel Domain-Based Entropies

Nalwaya

Das

Pachori

2022

Entropy

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Human dependence on computers is increasing day by day; thus, human interaction with computers must be more dynamic and contextual rather than static or generalized. The development of such devices requires knowledge of the emotional state of the user interacting with it; for this purpose, an emotion recognition system is required. Physiological signals, specifically, electrocardiogram (ECG) and electroencephalogram (EEG), were studied here for the purpose of emotion recognition. This paper proposes novel entropy-based features in the Fourier–Bessel domain instead of the Fourier domain, where frequency resolution is twice that of the latter. Further, to represent such non-stationary signals, the Fourier–Bessel series expansion (FBSE) is used, which has non-stationary basis functions, making it more suitable than the Fourier representation. EEG and ECG signals are decomposed into narrow-band modes using FBSE-based empirical wavelet transform (FBSE-EWT). The proposed entropies of each mode are computed to form the feature vector, which are further used to develop machine learning models. The proposed emotion detection algorithm is evaluated using publicly available DREAMER dataset. K-nearest neighbors (KNN) classifier provides accuracies of 97.84%, 97.91%, and 97.86% for arousal, valence, and dominance classes, respectively. Finally, this paper concludes that the obtained entropy features are suitable for emotion recognition from given physiological signals.

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Section: Introductionmentioning

confidence: 99%

Automated Emotion Identification Using Fourier–Bessel Domain-Based Entropies

Nalwaya

Das

Pachori

2022

Entropy

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Hybrid Transfer Learning Approach for Emotion Analysis of Occluded Facial Expressions

Pamod,

Charles,

Hewarathna

et al. 2024

Communications in Computer and Information Science

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Wearable Solutions Using Physiological Signals for Stress Monitoring on Individuals with Autism Spectrum Disorder (ASD): A Systematic Literature Review

Cano,

Cubillos,

Alfaro

et al. 2024

Sensors

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Some previous studies have focused on using physiological signals to detect stress in individuals with ASD through wearable devices, yet few have focused on how to design such solutions. Wearable technology may be a valuable tool to aid parents and caregivers in monitoring the emotional states of individuals with ASD who are at high risk of experiencing very stressful situations. However, effective wearable devices for individuals with ASD may need to differ from solutions for those without ASD. People with ASD often have sensory sensitivity and may, therefore, not tolerate certain types of accessories and experience discomfort when using them. We used the Scopus, PubMed, WoS, and IEEE-Xplore databases to search for studies published from 2014 to 2024 to answer four research questions related to wearable solutions for individuals with ASD, physiological parameters, and techniques/processes used for stress detection. Our review found 31 articles; not all studies considered individuals with ASD, and some were beyond the scope of this review. Most of the studies reviewed are based on cardiac activity for stress monitoring using photoplethysmography (PPG) and electrocardiography (ECG). However, limitations include small sample sizes, variability in study conditions, and the need for customization in stress detection algorithms. In addition, there is a need to customize the stress threshold due to the device’s high individual variability and sensitivity. The potential of wearable solutions for stress monitoring in ASD is evident, but challenges include the need for user-friendly and unobtrusive designs and integrating these technologies into comprehensive care plans.

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GSR Signals Features Extraction for Emotion Recognition

Cited by 3 publications

References 27 publications

Automated Emotion Identification Using Fourier–Bessel Domain-Based Entropies

Automated Emotion Identification Using Fourier–Bessel Domain-Based Entropies

Hybrid Transfer Learning Approach for Emotion Analysis of Occluded Facial Expressions

Wearable Solutions Using Physiological Signals for Stress Monitoring on Individuals with Autism Spectrum Disorder (ASD): A Systematic Literature Review

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