Affective Computing and Emotion-Sensing Technology for Emotion Recognition in Mood Disorders

Sinha, Nidhi

doi:10.1007/978-3-030-70111-6_16

Cited by 5 publications

(1 citation statement)

References 79 publications

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“…Image data sets are available to analyze emotions. DEAP, the publicly available human emotion dataset contains the ECG recording of various channels with interpreter labels for analysis [2] [19]. The growth of artificial intelligence and machine learning technology created various test scenarios for evaluating the human real affect status using standard datasets.…”

Section: Introductionmentioning

confidence: 99%

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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Section: Introductionmentioning

confidence: 99%

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 in Mood Disorders: Beyond Conventional Diagnostic Tools to Modern Technologies

Sinha,

Srivastava,

Ganesh

2024

The Springer Series in Applied Machine Learning

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Biomedical engineering in low- and middle-income settings: analysis of current state, challenges and best practices

et al. 2022

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Supporting the expansion of best practices in Biomedical Engineering (BME) can facilitate pathway toward the providing universal health coverage and more equitable and accessible healthcare technologies, especially in low- and middle-income (LMI) settings. These best practices can act as drivers of change and may involve scientific-technological issues, human intervention during technology development, educational aspects, social performance management for improved interactions along the medical technology life cycle, methods for managing resources and approaches for the establishment of regulatory frameworks.The aim of our study was to identify weaknesses and strengths of the scientific, technological, socio-political, regulatory and educational landscape in BME in LMI resource settings. We thus analysed the current state-of-the-art through six dimensions considered fundamental for advancing quality and equity in healthcare: 1) relevant and 2) emergent technologies, 3) new paradigms in medical technology development, 4) innovative BME education, 5) regulation and standardization for novel approaches, and 6) policy making. In order to evaluate and compare their relevance, maturity and implementation challenges, they were assessed through a questionnaire to which 100 professionals from 35 countries with recognized experience in the field of BME and its application to LMI settings responded.The results are presented and discussed, highlighting the main challenges and pinpointing relevant areas where intervention, including local lobbying and international promotion of best practices is necessary. We were also able to identify areas where minimal effort is required to make big changes in global health.

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Affective Computing and Emotion-Sensing Technology for Emotion Recognition in Mood Disorders

Cited by 5 publications

References 79 publications

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

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

Affective Computing in Mood Disorders: Beyond Conventional Diagnostic Tools to Modern Technologies

Biomedical engineering in low- and middle-income settings: analysis of current state, challenges and best practices

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