RETRACTED ARTICLE: A review of Deep Learning based methods for Affect Analysis using Physiological Signals

Garg, Divya; Verma, Gyanendra K.; Singh, Awadhesh Kumar

doi:10.1007/s11042-023-14354-9

Cited by 12 publications

(3 citation statements)

References 151 publications

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“…Secondly, in educational technology, deep learning-based methods for analyzing physiological signals (Garg et al, 2023) and EEG-based emotion recognition approaches (Li et al, 2023) can be employed. By utilizing physiological signals and EEG data, it becomes possible to recognize learners' emotional states and adjust teaching strategies accordingly.…”

Section: Implications For Education Technologymentioning

confidence: 99%

Affective Computing Research Progress and Its Implications for Education Technology: A Bibliometric Analysis Based on Web of Science via VOSviewer

Jiang,

Huang

2023

Atlantis Highlights in Computer Sciences

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Affective Computing (AC) is a dynamic and evolving research field. This paper presents a bibliometric analysis of 1428 publications related to affective computing, extracted from the Web of Science database, using the VOSviewer software. Through an examination of the existing literature, the study investigates the quantity of AC publications, research countries, important institutions, and leading authors. Co-citation analysis reveals that IEEE Transactions on Affective Computing is the most influential source of literature. Keyword co-occurrence and clustering analysis identify five main research directions in AC: emotion recognition, physiological signal, human-computer interaction, deep learning, and electroencephalography. Lastly, the paper provides relevant recommendations for AC research in educational technology, focusing on personalized learning experiences, affective feedback, emotion recognition, and affective robots.

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Section: Implications For Education Technologymentioning

confidence: 99%

Affective Computing Research Progress and Its Implications for Education Technology: A Bibliometric Analysis Based on Web of Science via VOSviewer

Jiang,

Huang

2023

Atlantis Highlights in Computer Sciences

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“…This has led researchers to explore multimodal data combining various emotional indicators for more stable and comprehensive emotion recognition. Studies affirm that multimodal data offers a holistic view of emotional shifts, facilitating cross-verification among different data types ( Garg, Verma & Singh, 2023 ). Current research predominantly integrates physiological data like EEG and eye movement recordings with behavioral data to formulate advanced emotion recognition systems.…”

Section: Introductionmentioning

confidence: 99%

E-MFNN: an emotion-multimodal fusion neural network framework for emotion recognition

Guo,

Yang,

Lin

et al. 2024

PeerJ Computer Science

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Emotional recognition is a pivotal research domain in computer and cognitive science. Recent advancements have led to various emotion recognition methods, leveraging data from diverse sources like speech, facial expressions, electroencephalogram (EEG), electrocardiogram, and eye tracking (ET). This article introduces a novel emotion recognition framework, primarily targeting the analysis of users’ psychological reactions and stimuli. It is important to note that the stimuli eliciting emotional responses are as critical as the responses themselves. Hence, our approach synergizes stimulus data with physical and physiological signals, pioneering a multimodal method for emotional cognition. Our proposed framework unites stimulus source data with physiological signals, aiming to enhance the accuracy and robustness of emotion recognition through data integration. We initiated an emotional cognition experiment to gather EEG and ET data alongside recording emotional responses. Building on this, we developed the Emotion-Multimodal Fusion Neural Network (E-MFNN), optimized for multimodal data fusion to process both stimulus and physiological data. We conducted extensive comparisons between our framework’s outcomes and those from existing models, also assessing various algorithmic approaches within our framework. This comparison underscores our framework’s efficacy in multimodal emotion recognition. The source code is publicly available at https://figshare.com/s/8833d837871c78542b29.

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“…Nassani et al [15] investigated innovation performance in the healthcare industry through the use of innovation networks and improving people's standard of living. The application of AI in the medical field has evolved from its initial stage of knowledge-driven systems, such as assisting in disease diagnosis and treatment, to a stage driven by data, such as electronic medical records [16] and physiological signals [17]. However, it has also brought about a series of challenges [18,19].…”

Section: Introductionmentioning

confidence: 99%

The DIKWP (Data, Information, Knowledge, Wisdom, Purpose) Revolution: A New Horizon in Medical Dispute Resolution

Mei,

Duan

2024

Applied Sciences

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The doctor–patient relationship has received widespread attention as a significant global issue affecting people’s livelihoods. In clinical practice within the medical field, applying existing artificial intelligence (AI) technology presents issues such as uncontrollability, inconsistency, and lack of self-explanation capabilities, even raising concerns about ethics and morality. To address the problem of doctor–patient interaction differences arising from the doctor–patient diagnosis and treatment, we collected the textual content of doctor–patient dialogues in outpatient clinics of local first-class hospitals. We utilized case scenario analysis, starting from two specific cases: multi-patient visits with the same doctor and multi-doctor interaction differences with the same patient. By capturing the external interactions and the internal thought processes, we unify the external expressions and internal subjective cognition in doctor–patient interactions into interactions between data, information, knowledge, wisdom, and purpose (DIKWP) models. We propose a DIKWP semantic model for the doctor–patient interactions on both sides, including a DIKWP content model and a DIKWP cognitive model, to achieve transparency throughout the entire doctor–patient interaction process. We semantically–bidirectionally map the diagnostic discrepancy space to DIKWP uncertainty and utilize a purpose-driven DIKWP semantic fusion transformation technique to disambiguate the uncertainty problem. Finally, we select four traditional methods for qualitative and quantitative comparison with our proposed method. The results show that our method performs better in content and uncertainty handling. Overall, our proposed DIKWP semantic model for doctor–patient interaction processing breaks through the uncertainty limitations of natural language semantics in terms of interpretability, enhancing the transparency and interpretability of the medical process. It will help bridge the cognitive gap between doctors and patients, easing medical disputes.

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RETRACTED ARTICLE: A review of Deep Learning based methods for Affect Analysis using Physiological Signals

Cited by 12 publications

References 151 publications

Affective Computing Research Progress and Its Implications for Education Technology: A Bibliometric Analysis Based on Web of Science via VOSviewer

Affective Computing Research Progress and Its Implications for Education Technology: A Bibliometric Analysis Based on Web of Science via VOSviewer

E-MFNN: an emotion-multimodal fusion neural network framework for emotion recognition

The DIKWP (Data, Information, Knowledge, Wisdom, Purpose) Revolution: A New Horizon in Medical Dispute Resolution

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