Retracted: Human Emotion Recognition Based on Galvanic Skin Response Signal Feature Selection and SVM

Li, Mingyang; Fan, Di; Zhang, Xiaohan; Gong, Xiaopeng

doi:10.1109/icscse.2016.0051

Cited by 47 publications

(25 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…A different method was carried out by Wei, who attempted to classify emotion into anger, fear, joy, sorrow, acceptance, rejection, surprise and expectancy. The research obtained a result with reasonable accuracy as much as 80% using SVM algorithm which was processed by Sliding Windows technique [12]. Using different approach, Guo also tried to classify human emotion into amusement, fear, relaxation, and sadness using Sliding Windows technique with the lag as much as 20 points.…”

Section: Introductionmentioning

confidence: 99%

“…Within this decade, a lot of researchers on emotion detection have been conducted. The algorithm of various detection tools such as GSR, electroencephalogram (EEG) and other devices have been made [3], [4], [12]. For example, EEG is used for measuring the change of positive and negative emotion of the subject who is induced through an IDFA or qDFA video.…”

Section: Introductionmentioning

confidence: 99%

“…In a different approach, Liu attempted to classify emotional status into happiness, grief, fear, anger, and calm using GSR data. The best accuracy level obtained by Liu was 66.67% through SVM algorithm with kernel radial based function (RBF) [12]. Recently, the Sliding Window technique has often been used in physiological data classification.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Galvanic Skin Response Data Classification for Emotion Detection

Setyohadi

Kusrohmaniah

Gunawan

et al. 2018

IJECE

View full text Add to dashboard Cite

Emotion detection is a very exhausting job and needs a complicated process; moreover, these processes also require the proper data training and appropriate algorithm. The process involves the experimental research in psychological experiment and classification methods. This paper describes a method on detection emotion using Galvanic Skin Response (GSR) data. We used the Positive and Negative Affect Schedule (PANAS) method to get a good data training. Furthermore, Support Vector Machine and a correct preprocessing are performed to classify the GSR data. To validate the proposed approach, Receiver Operating Characteristic (ROC) curve, and accuracy measurement are used. Our method shows that the accuracy is about 75.65% while ROC is about 0.8019. It means that the emotion detection can be done satisfactorily and well performed.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Galvanic Skin Response Data Classification for Emotion Detection

Setyohadi

Kusrohmaniah

Gunawan

et al. 2018

IJECE

View full text Add to dashboard Cite

show abstract

“…Many physiological modalities and features have been evaluated for ER, namely Electroencephalography (EEG) [ 28 , 29 , 30 ], Electrocardiography (ECG) [ 31 , 32 , 33 ], Electrodermal Activity (EDA) [ 34 , 35 , 36 ], Respiration (RESP) [ 26 ], Blood Volume Pulse (BVP) [ 26 , 35 ] and Temperature (TEMP) [ 26 ]. Multi-modal approaches have prevailed; however, there is still no clear evidence of which feature combinations and physiological signals are the most relevant.…”

Section: State Of the Artmentioning

confidence: 99%

Emotion Assessment Using Feature Fusion and Decision Fusion Classification Based on Physiological Data: Are We There Yet?

Bota

Wang²,

Fred

et al. 2020

Sensors

View full text Add to dashboard Cite

Emotion recognition based on physiological data classification has been a topic of increasingly growing interest for more than a decade. However, there is a lack of systematic analysis in literature regarding the selection of classifiers to use, sensor modalities, features and range of expected accuracy, just to name a few limitations. In this work, we evaluate emotion in terms of low/high arousal and valence classification through Supervised Learning (SL), Decision Fusion (DF) and Feature Fusion (FF) techniques using multimodal physiological data, namely, Electrocardiography (ECG), Electrodermal Activity (EDA), Respiration (RESP), or Blood Volume Pulse (BVP). The main contribution of our work is a systematic study across five public datasets commonly used in the Emotion Recognition (ER) state-of-the-art, namely: (1) Classification performance analysis of ER benchmarking datasets in the arousal/valence space; (2) Summarising the ranges of the classification accuracy reported across the existing literature; (3) Characterising the results for diverse classifiers, sensor modalities and feature set combinations for ER using accuracy and F1-score; (4) Exploration of an extended feature set for each modality; (5) Systematic analysis of multimodal classification in DF and FF approaches. The experimental results showed that FF is the most competitive technique in terms of classification accuracy and computational complexity. We obtain superior or comparable results to those reported in the state-of-the-art for the selected datasets.

show abstract

“…As the next-generation human-centered applications become more prevalent, robust and reliable affective sensing in face-to-face interactions is becoming more critical, especially important in supporting technologies of natural dialog interfaces, human behavior understanding [7], and health applications [8]. While tremendous effort has been developed in emotion recognition, majority of these research has focused mainly on developing algorithms for an individual's behavior in isolation (e.g., [9,10,11]). Only recently, some research have started to leverage the inter-dependencies between interlocutors to improve affect recognition of an individual.…”

Section: Introductionmentioning

confidence: 99%

Encoding Individual Acoustic Features Using Dyad-Augmented Deep Variational Representations for Dialog-level Emotion Recognition

Lee

2018

Interspeech 2018

View full text Add to dashboard Cite

Face-to-face dyadic spoken dialog is a fundamental unit of human interaction. Despite numerous empirical evidences in demonstrating interlocutor's behavior dependency in dyadic interactions, few technical works exist in leveraging the unique pattern of dynamics in task of advancing emotion recognition during face-to-face settings. In this work, we propose a framework of encoding an individual's acoustic features with dyadaugmented deep networks. The dyad-augmented deep networks includes a general variational deep Gaussian Mixture embedding network and a dyad-specific fine-tuned network. Our framework utilizes the augmented dyad-specific feature space to incorporate the unique behavior pattern emerged when two people interact. We perform dialog-level emotion regression tasks in both the CreativeIT and the NNIME databases. We obtain affect regression accuracy of 0.544 and 0.387 for activation and valence in the CreativeIT database (a relative improvement of 4.41% and 4.03% compared to using features without augmenting the dyad-specific representation), and we obtain 0.700 and 0.604 (4.48% and 4.14% relative improvement) for regressing activation and valence in the NNIME database.

show abstract

Retracted: Human Emotion Recognition Based on Galvanic Skin Response Signal Feature Selection and SVM

Cited by 47 publications

References 6 publications

Galvanic Skin Response Data Classification for Emotion Detection

Galvanic Skin Response Data Classification for Emotion Detection

Emotion Assessment Using Feature Fusion and Decision Fusion Classification Based on Physiological Data: Are We There Yet?

Encoding Individual Acoustic Features Using Dyad-Augmented Deep Variational Representations for Dialog-level Emotion Recognition

Contact Info

Product

Resources

About