Electronic Devices for Stress Detection in Academic Contexts during Confinement Because of the COVID-19 Pandemic

Acevedo, Cristhian Manuel Durán; Carrillo-Gómez, Jeniffer Katerine; Albarracín-Rojas, Camilo Andrés

doi:10.3390/electronics10030301

Cited by 16 publications

(16 citation statements)

References 77 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This was a study for the detection and identification of the volatile organic compound profiles emitted by the skin. The aim was to measure the student’s stress state during the exam and during the relaxation state, after the exam period [ 55 ].…”

Section: Resultsmentioning

confidence: 99%

Wearable Biosensor Technology in Education: A Systematic Review

Hernández-Mustieles,

Lima-Carmona,

Pacheco-Ramírez

et al. 2024

Sensors

View full text Add to dashboard Cite

Wearable Biosensor Technology (WBT) has emerged as a transformative tool in the educational system over the past decade. This systematic review encompasses a comprehensive analysis of WBT utilization in educational settings over a 10-year span (2012–2022), highlighting the evolution of this field to address challenges in education by integrating technology to solve specific educational challenges, such as enhancing student engagement, monitoring stress and cognitive load, improving learning experiences, and providing real-time feedback for both students and educators. By exploring these aspects, this review sheds light on the potential implications of WBT on the future of learning. A rigorous and systematic search of major academic databases, including Google Scholar and Scopus, was conducted in accordance with the PRISMA guidelines. Relevant studies were selected based on predefined inclusion and exclusion criteria. The articles selected were assessed for methodological quality and bias using established tools. The process of data extraction and synthesis followed a structured framework. Key findings include the shift from theoretical exploration to practical implementation, with EEG being the predominant measurement, aiming to explore mental states, physiological constructs, and teaching effectiveness. Wearable biosensors are significantly impacting the educational field, serving as an important resource for educators and a tool for students. Their application has the potential to transform and optimize academic practices through sensors that capture biometric data, enabling the implementation of metrics and models to understand the development and performance of students and professors in an academic environment, as well as to gain insights into the learning process.

show abstract

Section: Resultsmentioning

confidence: 99%

Wearable Biosensor Technology in Education: A Systematic Review

Hernández-Mustieles,

Lima-Carmona,

Pacheco-Ramírez

et al. 2024

Sensors

View full text Add to dashboard Cite

show abstract

“…However, there is evidence that the GSR signal could be used in conjunction with pattern recognition models for the identification of academic stress with an accuracy more than 95.00% (Durán Acevedo et al, 2021a;Rodríguez et al, 2020). New emerging methods, such as rPPG in combination with demographic data or e-nose, achieved an accuracy over 96.00% (Durán-Acevedo et al, 2021b;Morales-Fajardo et al, 2022).…”

Section: Discussionmentioning

confidence: 99%

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

et al. 2022

View full text Add to dashboard Cite

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.

show abstract

“…This study is focused on several peaks, max peak amplitude Not Available [40] Implemented Electronic Nose (E-nose) for stress detection through emotional sweat A graphic interface is designed Only measuring different signals for stress detection A graphic interface is designed [41] Wireless sensor node prototype implanted on the gloves of firemen for real-time stress detection…”

Section: Android Applicationmentioning

confidence: 99%

Cloud Server and Internet of Things Assisted System for Stress Monitoring

Singh

Gehlot

Rashid³

et al. 2021

Electronics

View full text Add to dashboard Cite

Currently, the Internet of Things (IoT) has gained attention for its capability for real-time monitoring. The advancement in sensor and wireless communication technology has led to the widespread adoption of IoT technology in distinct applications. The cloud server, in conjunction with the IoT, enables the visualization and analysis of real-time sensor data. The literature concludes that there is a lack of remote stress-monitoring devices available to assist doctors in observing the real-time stress status of patients in the hospital and in rehabilitation centers. To overcome this problem, we have proposed the use of the IoT and cloud-enabled stress devices to detect stress in a real-time environment. The IoT-enabled stress device establishes piconet communication with the master node to allow visualization of the sensory data on the cloud server. The threshold value (volt) for real-time stress detection by the stress device is identified by experimental analysis using MATLAB based on the results obtained from the performance of three different physical-stress generating tasks. In addition, the stress device is interfaced with the cloud server, and the sensor data are recorded on the cloud server. The sensor data logged into the cloud server can be utilized for future analysis.

show abstract

Electronic Devices for Stress Detection in Academic Contexts during Confinement Because of the COVID-19 Pandemic

Cited by 16 publications

References 77 publications

Wearable Biosensor Technology in Education: A Systematic Review

Wearable Biosensor Technology in Education: A Systematic Review

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

Cloud Server and Internet of Things Assisted System for Stress Monitoring

Contact Info

Product

Resources

About