A Survey on Affective Computing for Psychological Emotion Recognition

Bakkialakshmi, V. S.; SudalaiMuthu, T

doi:10.1109/iceeccot52851.2021.9707947

Cited by 7 publications

(6 citation statements)

References 30 publications

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“…The process is initiated by reading the AMIGOS dataset, in which the physiological signals are considered. The data was collected from 40 participants the exposure to 20 unique videos [17]. The preprocessed physiological data such as ECG, EEG, and GSR contain the unique reflection points gathered from the participants during the Video exposure.…”

Section: Methodology 31 System Architecturementioning

confidence: 99%

AMIGOS: a robust emotion detection framework through Gaussian ResiNet

Bakkialakshmi

SudalaiMuthu

2022

Bulletin EEI

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Affective computing is the study of the deep extraction of emotional impacts that triggers humans for various reasons. Emotions directly reflect on human behaviour. The proposed analysis is inclined towards deep emotion extraction through a novel concept with less computation time. Designing a robust analysis model is focused on here. AMIGOS dataset on affect, and personality modelling is considered here. A Novel Gaussian ResiNet (GRN) algorithm is evaluated here. Any changes in the emotions of humans are the brainy response given to the actions faced. The features of the given physiological factors are considered for analysis, further with GMM-ResiNet (GRN) a low computational structure is used for classification. The Novel Gaussian ResiNet (GRN) is created from the given dataset for similar feature validations. The system predicts the correlated relative data from the training set and testing set and achieved the performance metrics using error rate (ER), Algorithm Computation Time (ACT), Full Computation Time (FCT), Accuracy (AUC) etc. Novel Gaussian ResiNet (GRN) is created and tested with processed data of the AMIGOS dataset. The model created is validated with state-of-art approaches and achieved an accuracy of 92.6%.

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Section: Methodology 31 System Architecturementioning

confidence: 99%

AMIGOS: a robust emotion detection framework through Gaussian ResiNet

Bakkialakshmi

SudalaiMuthu

2022

Bulletin EEI

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“…shows the Self-Assessment manikins chart for Violence, arousal and dominance. [24] SAM tool consists of an affective slider designed to represent the pictorial representation of pleasure on the top row, arousal in the middle row, and further dominance on the bottom scale is defined. During the emotion analysis process, the participants are allowed to make a marking in the chart shown above to make them self-assess the exposed inputs.…”

Section: Data Collectionmentioning

confidence: 99%

“…[16] the author used the Conventional LSTM model and achieved an accuracy of 82% on affective computing. Liu, et al, (2022) [24] discussed the Deep Convolution Neural Network for affect state analysis and achieved an accuracy of 92.5%. From the table above, the proposed EmoNet_ANN (ENA) model achieved an accuracy of 92.95% with multiple parameters with reduced computations.…”

Section: Error Histogramsmentioning

confidence: 99%

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Emo-Net Artificial Neural Network: A Robust Affective Computing Prediction System for Emotional Psychology Using Amigos

S¹,

Sudalaimuthu²

2022

INDJCSE

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Affective computing has become an increasingly popular area of study that focuses on developing a robust system that automatically recognizes human emotions. Any changes in human emotion directly impact brain stimulation and physiological parameters. Emotions are critical parameters that directly impact the behaviour of an individual. Collecting the neurophysiological data from humans is utilized here. The ultimate goal of the presented work is to develop a robust methodology to predict human real affect. The present system considers the Amigos data set in which physiological signals such as ECG, EEG, and Galvanic Skin Response (GSR) are considered. The proposed framework is modelled using Gaussian mixture models to produce an expectation-maximization technique (GEM). Along with the measurement of statistical parameters such as mean, standard deviation, and Sigma, these are helpful for the system to identify the real class. The comprehensive analysis of various participant data collected through the AMIGOS dataset was split into training data and testing data. The raw data preprocessing using the Synthetic Minority Oversampling Technique (SMOTE) model in which the data has cleaned up random values or removed junk values or removed. Since data is provided to the GEM algorithm in which the expectation-maximization parameters such as mean median standard deviation and sigmoid are calculated. Based on the evaluated model, part of the test signals are given from the amigo dataset. The system can compare an interpreter to validate the parameters-based emotion labelling. A novel EmoNet_ANN (ENA) system is further improved by including deep learning models to find the detailed covariate values helpful to identify the personality traits of the participants. The proposed optimized Novel EmoNet_ANN (ENA) perceives the accuracy benefit of 92.5% with less computational time. A novel EmoNet_ANN(ENA) Proposed system is comparatively studied and further improved by evaluating deep learning models to find the detailed covariate values present in the data set. The statistical measures on Sigma, Variance from the GEM model concerning the correlation metric from ANN are further used to validate the emotion detection. Accuracy, Precision, Recall and F1score form the analysis module which is used to determine the overall performance statistics. Emotion analysis and affect impacted parameters are modelled using Novel EmoNet_ANN (ENA). The AMIGOS dataset analyses physiological signals to determine the actual emotions. With reduced computation time and iteration run till Zero error on complete analysis.

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“…The algorithm is proposed for multi-label learning to aid in investigating emotional effects in several modalities. Diverse modality scenarios allow the emotional effect components to be investigated from several perspectives regarding the primary polarity of happy and sad emotions [6].…”

Section: Introductionmentioning

confidence: 99%

Affective analysis in machine learning using AMIGOS with Gaussian expectation-maximization model

Kaliappan,

Sudalaiyadumperumal,

Thalavaipillai

2024

IJRES

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<p>Investigating human subjects is the goal of predicting human emotions in the stock market. A significant number of psychological effects require (feelings) to be produced, directly releasing human emotions. The development of effect theory leads one to believe that one must be aware of one's sentiments and emotions to forecast one's behavior. The proposed line of inquiry focuses on developing a reliable model incorporating neurophysiological data into actual feelings. Any change in emotional affect will directly elicit a response in the body's physiological systems. This approach is named after the notion of Gaussian mixture models (GMM). The statistical reaction following data processing, quantitative findings on emotion labels, and coincidental responses with training samples all directly impact the outcomes that are accomplished. In terms of statistical parameters such as population mean and standard deviation, the suggested method is evaluated compared to a technique considered to be state-of-the-art. The proposed system determines an individual's emotional state after a minimum of 6 iterative learning using the Gaussian expectation-maximization (GEM) statistical model, in which the iterations tend to continue to zero error. Perhaps each of these improves predictions while simultaneously increasing the amount of value extracted.</p>

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A Survey on Affective Computing for Psychological Emotion Recognition

Cited by 7 publications

References 30 publications

AMIGOS: a robust emotion detection framework through Gaussian ResiNet

AMIGOS: a robust emotion detection framework through Gaussian ResiNet

Emo-Net Artificial Neural Network: A Robust Affective Computing Prediction System for Emotional Psychology Using Amigos

Affective analysis in machine learning using AMIGOS with Gaussian expectation-maximization model

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