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Deep Learning Models for Stress Analysis in University Students: A Sudoku-Based Study

Sensors (Basel, Switzerland), 2023-07, Vol.23 (13), p.6099 [Peer Reviewed Journal]

COPYRIGHT 2023 MDPI AG ;2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;2023 by the authors. 2023 ;ISSN: 1424-8220 ;EISSN: 1424-8220 ;DOI: 10.3390/s23136099 ;PMID: 37447948

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Title:
Deep Learning Models for Stress Analysis in University Students: A Sudoku-Based Study
Author: Chen, Qicheng ; Lee, Boon Giin
Subjects: Academic achievement ; Accuracy ; Algorithms ; Anxiety ; Asian students ; Cardiac stress tests ; Classification ; Datasets ; Deep Learning ; Electrocardiogram ; Electrocardiography ; Electroencephalography ; Emotion recognition ; Emotional factors ; Emotions ; Extracurricular activities ; Humans ; Monitoring ; Neural networks ; Physiological aspects ; Physiology ; Problem solving ; Registration ; Self Report ; signal processing ; Stress analysis ; stress detection ; Students ; Students - psychology ; Support vector machines ; Telemedicine ; Universities ; University students ; wearable sensing
Is Part Of: Sensors (Basel, Switzerland), 2023-07, Vol.23 (13), p.6099
Description: Due to the phenomenon of "involution" in China, the current generation of college and university students are experiencing escalating levels of stress, both academically and within their families. Extensive research has shown a strong correlation between heightened stress levels and overall well-being decline. Therefore, monitoring students' stress levels is crucial for improving their well-being in educational institutions and at home. Previous studies have primarily focused on recognizing emotions and detecting stress using physiological signals like ECG and EEG. However, these studies often relied on video clips to induce various emotional states, which may not be suitable for university students who already face additional stress to excel academically. In this study, a series of experiments were conducted to evaluate students' stress levels by engaging them in playing Sudoku games under different distracting conditions. The collected physiological signals, including PPG, ECG, and EEG, were analyzed using enhanced models such as LRCN and self-supervised CNN to assess stress levels. The outcomes were compared with participants' self-reported stress levels after the experiments. The findings demonstrate that the enhanced models presented in this study exhibit a high level of proficiency in assessing stress levels. Notably, when subjects were presented with Sudoku-solving tasks accompanied by noisy or discordant audio, the models achieved an impressive accuracy rate of 95.13% and an F1-score of 93.72%. Additionally, when subjects engaged in Sudoku-solving activities with another individual monitoring the process, the models achieved a commendable accuracy rate of 97.76% and an F1-score of 96.67%. Finally, under comforting conditions, the models achieved an exceptional accuracy rate of 98.78% with an F1-score of 95.39%.
Publisher: Switzerland: MDPI AG
Language: English
Identifier: ISSN: 1424-8220
EISSN: 1424-8220
DOI: 10.3390/s23136099
PMID: 37447948
Source: GFMER Free Medical Journals
MEDLINE
PubMed Central
ROAD: Directory of Open Access Scholarly Resources
ProQuest Central
DOAJ Directory of Open Access Journals

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Deep Learning Models for Stress Analysis in University Students: A Sudoku-Based Study

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