A Review of AI Cloud and Edge Sensors, Methods, and Applications for the Recognition of Emotional, Affective and Physiological States

Advances in Ga2O3-based memristor devices, modeling, properties, and applications for low power neuromorphic computing

About a decade ago, gallium oxide (Ga2O3) was found to be a very attractive ultrawide-bandgap (4.6-4.9 eV) semiconductor for next-generation low-power devices. Ga2O3 materials have attracted a lot of scientific and technical interest because of their outstanding properties and numerous application opportunities in the field of semiconductor based memristor technology. This review is focused on Ga2O3 thin-film memristors for smart technologies. The capacitance behavior of memristors is very important for adapting nonlinear memristor responses. Also, this comprehensive review explores in depth the ideas, device construction, and manufacturing procedures for Ga2O3-based memristor devices. To improve the device's behavior and performance improvement, a detailed analysis of many modeling and simulation techniques is given. Also, advanced characterization techniques, such as electrical, structural, and thermal evaluations, for studying artificial optoelectronic synaptic characteristics, which are important for use in computational neuroscience, are discussed in detail. The synaptic activities revealed that learning and memory processes were aided by potentiation and depression similar to those found in biological synapses. The most notable accomplishment is the realization of quaternary memory storage in a single device. This idea is supported by empirical evidence and simulations, which demonstrate the possibility of storing and maintaining multiple memory states. This study establishes oxide semiconductor memristors as a doorway to quaternary memory storage and improved synaptic functioning, paving the way for optoelectronic synaptic devices with greater memory capacity.

From Neural Networks to Emotional Networks: A Systematic Review of EEG-Based Emotion Recognition in Cognitive Neuroscience and Real-World Applications

BACKGROUND/OBJECTIVES

This systematic review presents how neural and emotional networks are integrated into EEG-based emotion recognition, bridging the gap between cognitive neuroscience and practical applications.

METHODS

Following PRISMA, 64 studies were reviewed that outlined the latest feature extraction and classification developments using deep learning models such as CNNs and RNNs.

RESULTS

Indeed, the findings showed that the multimodal approaches were practical, especially the combinations involving EEG with physiological signals, thus improving the accuracy of classification, even surpassing 90% in some studies. Key signal processing techniques used during this process include spectral features, connectivity analysis, and frontal asymmetry detection, which helped enhance the performance of recognition. Despite these advances, challenges remain more significant in real-time EEG processing, where a trade-off between accuracy and computational efficiency limits practical implementation. High computational cost is prohibitive to the use of deep learning models in real-world applications, therefore indicating a need for the development and application of optimization techniques. Aside from this, the significant obstacles are inconsistency in labeling emotions, variation in experimental protocols, and the use of non-standardized datasets regarding the generalizability of EEG-based emotion recognition systems.

DISCUSSION

These challenges include developing adaptive, real-time processing algorithms, integrating EEG with other inputs like facial expressions and physiological sensors, and a need for standardized protocols for emotion elicitation and classification. Further, related ethical issues with respect to privacy, data security, and machine learning model biases need to be much more proclaimed to responsibly apply research on emotions to areas such as healthcare, human-computer interaction, and marketing.

CONCLUSIONS

This review provides critical insight into and suggestions for further development in the field of EEG-based emotion recognition toward more robust, scalable, and ethical applications by consolidating current methodologies and identifying their key limitations.

An Ensemble Deep Learning Approach for EEG-Based Emotion Recognition Using Multi-Class CSP

In recent years, significant advancements have been made in the field of brain-computer interfaces (BCIs), particularly in the area of emotion recognition using EEG signals. The majority of earlier research in this field has missed the spatial-temporal characteristics of EEG signals, which are critical for accurate emotion recognition. In this study, a novel approach is presented for classifying emotions into three categories, positive, negative, and neutral, using a custom-collected dataset. The dataset used in this study was specifically collected for this purpose from 16 participants, comprising EEG recordings corresponding to the three emotional states induced by musical stimuli. A multi-class Common Spatial Pattern (MCCSP) technique was employed for the processing stage of the EEG signals. These processed signals were then fed into an ensemble model comprising three autoencoders with Convolutional Neural Network (CNN) layers. A classification accuracy of 99.44 ± 0.39% for the three emotional classes was achieved by the proposed method. This performance surpasses previous studies, demonstrating the effectiveness of the approach. The high accuracy indicates that the method could be a promising candidate for future BCI applications, providing a reliable means of emotion detection.

Requirements and Barriers for Human-Centered SMEs

With the advantages of new technologies and rising demand from customers, it is necessary to improve the manufacturing process. This necessity was recognized by the industry; therefore, the concept of Industry 4.0 has been implemented in various areas of manufacturing and services. The backbone and main aspect of Industry 4.0 is digitalization and the implementation of technologies into processes. While this concept helps manufacturers with the modernization and optimization of many attributes of the processes, Industry 5.0 takes a step further and brings importance to the human factor of industry practice, together with sustainability and resilience. The concept of Industry 5.0 contributes to the idea of creating a sustainable, prosperous, and human-friendly environment within companies. The main focus of the article is to analyze the existing literature regarding what is missing from the successful implementation of human centricity into industry practice, namely in small and medium-sized factories (SMEs). These findings are then presented in the form of requirements and barriers for the implementation of human centricity into SME factories, which can serve as guidelines for implementing human-centered manufacturing using axiomatic design theory in SMEs, which can serve as a roadmap for practitioners.

Personality Trait Recognition using ECG Spectrograms and Deep Learning

This paper presents an innovative approach to recognizing personality traits using deep learning (DL) methods applied to electrocardiogram (ECG) signals. The research explores the potential of ECG-derived spectrograms as informative features in detecting the Big Five personality traits model encompassing extraversion, neuroticism, agreeableness, conscientiousness, and openness. Optimal window sizes for spectrogram generation are determined, and a convolutional neural network (CNN), specifically Resnet-18, and visual transformer (ViT) are employed for feature extraction and personality trait classification. The study utilizes the publicly available ASCERTAIN dataset, which comprises various physiological signals, including ECG recordings, collected from 58 participants while presenting video stimuli categorized by valence and arousal levels. The outcomes of this study demonstrate noteworthy performance in personality trait classification, consistently achieving F1-scores exceeding 0.9 across different window sizes and personality traits. These results emphasize the viability of ECG signal spectrograms as a valuable modality for personality trait recognition, with Resnet-18 exhibiting effectiveness in discerning distinct personality traits.

An Escape Game on University Students' Mental Health During the COVID-19 Pandemic: Cocreation Study

BACKGROUND

The COVID-19 pandemic has had a severe impact on students' mental health. Interventions are needed to promote their psychological well-being and prevent mental illnesses in the aftermath of this unprecedented situation. Digital escape games can be an effective tool to support students' mental health. A cocreation approach can improve the acceptability of these interventions by involving different stakeholders (eg, end users, game designers, and health professionals) to obtain audience-specific games.

OBJECTIVE

This study aims to describe the process of testing and optimizing the game "EscapeCovid" on students' mental health, to serve as a model for the cocreation of future similar interventions.

METHODS

The PRODUCES (Problem, Objective, Design, End Users, Cocreators, Evaluation, Scalability) framework was used. Cocreation steps (test and optimization) were detailed for replicability. A total of 45 students tested a pilot version of the game, with 10 undergoing a semistructured interview. Meetings with a group of stakeholders and brainwriting were organized to optimize the game.

RESULTS

We produced a new version of the game incorporating the suggestions provided by student testers and following the stakeholders' guidelines. Improvements were made to both the content and the form of the new version of the pilot game. The storyline, including the protagonist and the scenes, was adapted to the student population.

CONCLUSIONS

Our results suggested that cocreation can contribute to the design of more widely accepted interventions aimed at promoting mental health and preventing psychological disorders. Results also suggest that an end user-centered approach can facilitate intervention tailoring. When conceiving a health-related escape game for students, we recommend using the cocreation approach to enhance players' experience, thus positively influencing their learning process and overall well-being.

Special Issue "Emotion Intelligence Based on Smart Sensing"

Emotional intelligence is essential to maintaining human relationships in communities, organizations, and societies [...].