Hybrid deep convolutional model-based emotion recognition using multiple physiological signals

Akbulut, Fatma Patlar

doi:10.1080/10255842.2022.2032682

Cited by 18 publications

(5 citation statements)

References 69 publications

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“…Recently, deep learning-based analysis of sentiments has achieved great success in different areas such as audiovisual [13], image [14], and sequential data [15] processing. While increasing the training data for traditional machine learning techniques does not always improve the performance, in deep learning, the success chance increases as the training data diversifies and increases.…”

Section: Natural Language Processing and Sentiment Analysismentioning

confidence: 99%

Deep learning-based user experience evaluation in distance learning

et al. 2023

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The Covid-19 pandemic caused uncertainties in many different organizations, institutions gained experience in remote working and showed that high-quality distance education is a crucial component in higher education. The main concern in higher education is the impact of distance education on the quality of learning during such a pandemic. Although this type of education may be considered effective and beneficial at first glance, its effectiveness highly depends on a variety of factors such as the availability of online resources and individuals’ financial situations. In this study, the effectiveness of e-learning during the Covid-19 pandemic is evaluated using posted tweets, sentiment analysis, and topic modeling techniques. More than 160,000 tweets, addressing conditions related to the major change in the education system, were gathered from Twitter social network and deep learning-based sentiment analysis models and topic models based on latent dirichlet allocation (LDA) algorithm were developed and analyzed. Long short term memory-based sentiment analysis model using word2vec embedding was used to evaluate the opinions of Twitter users during distance education and also, a topic model using the LDA algorithm was built to identify the discussed topics in Twitter. The conducted experiments demonstrate the proposed model achieved an overall accuracy of 76%. Our findings also reveal that the Covid-19 pandemic has negative effects on individuals 54.5% of tweets were associated with negative emotions whereas this was relatively low on emotion reports in the YouGov survey and gender-rescaled emotion scores on Twitter. In parallel, we discuss the impact of the pandemic on education and how users’ emotions altered due to the catastrophic changes allied to the education system based on the proposed machine learning-based models.

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Section: Natural Language Processing and Sentiment Analysismentioning

confidence: 99%

Deep learning-based user experience evaluation in distance learning

et al. 2023

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“…Experience sampling is one of the methods used to analyze emotional reactions [18] and habits. It aims to create a rich overview of the phenomena by examining the subject participating in the study, and the answers to a couple of questions are sought in the following process.…”

Section: Experience Sampling Approachmentioning

confidence: 99%

“…In 2013 22nd ITC specialist seminar on energy efficient and green networking SEEGN) (pp. [13][14][15][16][17][18]. IEEE.…”

Section: Appendix a Primary Studies Selectedmentioning

confidence: 99%

“…According to Colligan and Higgins [15], stress in the workplace is detrimental to the well-being of employees and can lead to increased absenteeism, organizational dysfunction, and reduced productivity. Many studies have shown that attention interventions reliably reduce both general psychological stress [16][17][18] and occupational distress [19]. Furthermore, other evidence suggests that mindfulness is associated with coping success during stressful events [20].…”

Section: Introductionmentioning

confidence: 99%

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Stress Detection Using Experience Sampling: A Systematic Mapping Study

Dogan

Akbulut

Çatal

et al. 2022

IJERPH

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Stress has been designated the “Health Epidemic of the 21st Century” by the World Health Organization and negatively affects the quality of individuals’ lives by detracting most body systems. In today’s world, different methods are used to track and measure various types of stress. Among these techniques, experience sampling is a unique method for studying everyday stress, which can affect employees’ performance and even their health by threatening them emotionally and physically. The main advantage of experience sampling is that evaluating instantaneous experiences causes less memory bias than traditional retroactive measures. Further, it allows the exploration of temporal relationships in subjective experiences. The objective of this paper is to structure, analyze, and characterize the state of the art of available literature in the field of surveillance of work stress via the experience sampling method. We used the formal research methodology of systematic mapping to conduct a breadth-first review. We found 358 papers between 2010 and 2021 that are classified with respect to focus, research type, and contribution type. The resulting research landscape summarizes the opportunities and challenges of utilizing the experience sampling method on stress detection for practitioners and academics.

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“…Especially, during the Covid-19 pandemic, online education has increased dramatically all around the world, therefore, it needs to be supported with innovative approaches more than ever. The literature contains plenty of studies that aim to detect only emotions [3,29] with similar approaches to this study. As valuable examples, Vatcharaphrueksadee et al [39] and Jaiswal et al [21] work with facial data and use CNN models to classify emotions.…”

Section: Introductionmentioning

confidence: 99%

Analysis of facial emotion expression in eating occasions using deep learning

Yıldırım

Akbulut

Çatal

2023

Multimed Tools Appl

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Eating is experienced as an emotional social activity in any culture. There are factors that influence the emotions felt during food consumption. The emotion felt while eating has a significant impact on our lives and affects different health conditions such as obesity. In addition, investigating the emotion during food consumption is considered a multidisciplinary problem ranging from neuroscience to anatomy. In this study, we focus on evaluating the emotional experience of different participants during eating activities and aim to analyze them automatically using deep learning models. We propose a facial expression-based prediction model to eliminate user bias in questionnaire-based assessment systems and to minimize false entries to the system. We measured the neural, behavioral, and physical manifestations of emotions with a mobile app and recognize emotional experiences from facial expressions. In this research, we used three different situations to test whether there could be any factor other than the food that could affect a person’s mood. We asked users to watch videos, listen to music or do nothing while eating. This way we found out that not only food but also external factors play a role in emotional change. We employed three Convolutional Neural Network (CNN) architectures, fine-tuned VGG16, and Deepface to recognize emotional responses during eating. The experimental results demonstrated that the fine-tuned VGG16 provides remarkable results with an overall accuracy of 77.68% for recognizing the four emotions. This system is an alternative to today’s survey-based restaurant and food evaluation systems.

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Hybrid deep convolutional model-based emotion recognition using multiple physiological signals

Cited by 18 publications

References 69 publications

Deep learning-based user experience evaluation in distance learning

Deep learning-based user experience evaluation in distance learning

Stress Detection Using Experience Sampling: A Systematic Mapping Study

Analysis of facial emotion expression in eating occasions using deep learning

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