Academic stress detection on university students during COVID-19 outbreak by using an electronic nose and the galvanic skin response

A Review of AIoT-Based Human Activity Recognition: From Application to Technique

This scoping review paper redefines the Artificial Intelligence-based Internet of Things (AIoT) driven Human Activity Recognition (HAR) field by systematically extrapolating from various application domains to deduce potential techniques and algorithms. We distill a general model with adaptive learning and optimization mechanisms by conducting a detailed analysis of human activity types and utilizing contact or non-contact devices. It presents various system integration mathematical paradigms driven by multimodal data fusion, covering predictions of complex behaviors and redefining valuable methods, devices, and systems for HAR. Additionally, this paper establishes benchmarks for behavior recognition across different application requirements, from simple localized actions to group activities. It summarizes open research directions, including data diversity and volume, computational limitations, interoperability, real-time recognition, data security, and privacy concerns. Finally, we aim to serve as a comprehensive and foundational resource for researchers delving into the complex and burgeoning realm of AIoT-enhanced HAR, providing insights and guidance for future innovations and developments.

Machine Learning, Deep Learning, and Data Preprocessing Techniques for Detecting, Predicting, and Monitoring Stress and Stress-Related Mental Disorders: Scoping Review

BACKGROUND

Mental stress and its consequent mental health disorders (MDs) constitute a significant public health issue. With the advent of machine learning (ML), there is potential to harness computational techniques for better understanding and addressing mental stress and MDs. This comprehensive review seeks to elucidate the current ML methodologies used in this domain to pave the way for enhanced detection, prediction, and analysis of mental stress and its subsequent MDs.

OBJECTIVE

This review aims to investigate the scope of ML methodologies used in the detection, prediction, and analysis of mental stress and its consequent MDs.

METHODS

Using a rigorous scoping review process with PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines, this investigation delves into the latest ML algorithms, preprocessing techniques, and data types used in the context of stress and stress-related MDs.

RESULTS

A total of 98 peer-reviewed publications were examined for this review. The findings highlight that support vector machine, neural network, and random forest models consistently exhibited superior accuracy and robustness among all ML algorithms examined. Physiological parameters such as heart rate measurements and skin response are prevalently used as stress predictors due to their rich explanatory information concerning stress and stress-related MDs, as well as the relative ease of data acquisition. The application of dimensionality reduction techniques, including mappings, feature selection, filtering, and noise reduction, is frequently observed as a crucial step preceding the training of ML algorithms.

CONCLUSIONS

The synthesis of this review identified significant research gaps and outlines future directions for the field. These encompass areas such as model interpretability, model personalization, the incorporation of naturalistic settings, and real-time processing capabilities for the detection and prediction of stress and stress-related MDs.

Central Countries' and Brazil's Contributions to Nanotechnology

Abstract:

Nanotechnology is a cornerstone of the scientific advances witnessed over the past few years. Nanotechnology applications are extensively broad, and an overview of the main trends worldwide can give an insight into the most researched areas and gaps to be covered. This document presents an overview of the trend topics of the three leading countries studying in this area, as well as Brazil for comparison. The data mining was made from the Scopus database and analyzed using the VOSviewer and Voyant Tools software. More than 44.000 indexed articles published from 2010 to 2020 revealed that the countries responsible for the highest number of published articles are The United States, China, and India, while Brazil is in the fifteenth position. Thematic global networks revealed that the standing-out research topics are health science, energy, wastewater treatment, and electronics. In a temporal observation, the primary topics of research are: India (2020), which was devoted to facing SARS-COV 2; Brazil (2019), which is developing promising strategies to combat cancer; China (2018), whit research on nanomedicine and triboelectric nanogenerators; the United States (2017) and the Global tendencies (2018) are also related to the development of triboelectric nanogenerators. The collected data are available on GitHub. This study demonstrates the innovative use of data-mining technologies to gain a comprehensive understanding of nanotechnology's contributions and trends and highlights the diverse priorities of nations in this cutting-edge field.

Perceived academic stress during a pandemic: Mediating role of coping strategies

COVID-19 rampant has impacted almost all sections of society, and the repercussions were mostly negative experiences for people and have resulted by way of disruption in their daily routines. Academics is one such vital section that has suffered directly because of the inaccessibility of a comfortable educational procedure. Due to a shift in the form of education, most of the student community failed to obtain routine and regular education as the government entirely shuttered educational facilities to limit the spread of disease. In this light, this research attempted to examine the amount of academic stress experienced by students during the COVID-19 Pandemic and the strategies they have adopted to cope with this unheard type of uncertain situation. The findings of the study indicated substantial variations in Academic Stress, Exam Anxiety, and Coping Strategies across various demographic characteristics of the respondents. Another significant finding is that students from poor socio-economic backgrounds and those seeking post-graduate courses are more stressed. As an inference, it is also opined that to mitigate the impact of the COVID-19 crisis on student performance and psychological well-being, special focus, or techniques for accommodating exam environments by the student should be implemented. To minimize stress, the study also proposed efficient coping techniques to lower the amount of stress in various academic tasks.

A clinical and technical methodological review on stress detection and sleep quality prediction in an academic environment

BACKGROUND

Mental health in recent times is a much talked about topic and its effects on the sleep health of the students are said to result in long-term health issues if not identified and resolved. Students who are subjected to psychological stress have often been reported to have lower sleep quality which together has affected the academic performance of the students.

OBJECTIVE

While stress has its adverse effect on students'quality of sleep, an effort is also made to identify standard techniques and tools to automatically assess stress levels and sleep quality in a non-invasive environment among students only. This article mainly focuses on the Clinical and technical methodology employed in stress level detection and sleep quality prediction among students.

METHODS

This study was conducted by examining all research studies conducted in the past with respect to students in an academic setting from year 2000 to early 2022. The papers under study where finalised based on different methodologies involved in stress level detection and sleep quality prediction considering both in unimodal and multimodal measurements.

RESULTS

While questionnaires and physiological signals are used as a standard measuring tool, it is mostly used in a unimodal environment to measure students' mental stress or sleep quality in academic settings.

CONCLUSION

This paper describes in detail the clinical aspect of the association between mental stress, sleep quality, and academic performance in students followed by technical aspects to analyse the stress levels and sleep quality both qualitatively and quantitatively in an academic environment.

Advances and challenges in the detection of academic stress and anxiety in the classroom: A literature review and recommendations

In recent years, stress and anxiety have been identified as two of the leading causes of academic underachievement and dropout. However, there is little work on the detection of stress and anxiety in academic settings and/or its impact on the performance of undergraduate students. Moreover, there is a gap in the literature in terms of identifying any computing, information technologies, or technological platforms that help educational institutions to identify students with mental health problems. This paper aims to systematically review the literature to identify the advances, limitations, challenges, and possible lines of research for detecting academic stress and anxiety in the classroom. Forty-four recent articles on the topic of detecting stress and anxiety in academic settings were analyzed. The results show that the main tools used for detecting anxiety and stress are psychological instruments such as self-questionnaires. The second most used method is acquiring and analyzing biological signals and biomarkers using commercial measurement instruments. Data analysis is mainly performed using descriptive statistical tools and pattern recognition techniques. Specifically, physiological signals are combined with classification algorithms. The results of this method for detecting anxiety and academic stress in students are encouraging. Using physiological signals reduces some of the limitations of psychological instruments, such as response time and self-report bias. Finally, the main challenge in the detection of academic anxiety and stress is to bring detection systems into the classroom. Doing so, requires the use of non-invasive sensors and wearable systems to reduce the intrinsic stress caused by instrumentation.

A deep learning-based prediction model of college students' psychological problem categories for post-epidemic era-Taking college students in Jiangsu Province, China as an example

For a long time, it takes a lot of time and energy for psychological workers to classify the psychological problems of college students. In order to quickly and efficiently understand the common psychological problems of college students in the region for real-time analysis in the post-epidemic era, 2,000 college students' psychological problems were selected as research data in the community question section of the "Su Xin" application, a psychological self-help and mutual aid platform for college students in Jiangsu Province. First, word segmentation, removal of stop words, establishment of word vectors, etc. were used for the preprocessing of research data. Secondly, it was divided into 9 common psychological problems by LDA clustering analysis, which also combined with previous researches. Thirdly, the text information was processed into word vectors and transferred to the Attention-Based Bidirectional Long Short-Term Memory Networks (AB-LSTM). The experimental results showed that the proposed model has a higher test accuracy of 78% compared with other models.