An Explainable Student Fatigue Monitoring Module with Joint Facial Representation

Online fatigue estimation is, inevitably, in demand as fatigue can impair the health of college students and lower the quality of higher education. Therefore, it is essential to monitor college students’ fatigue to diminish its adverse effects on the health and academic performance of college studen...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Vol. 23; no. 7; p. 3602
Main Authors: Li, Xiaomian, Lin, Jiaqin, Tian, Zhiqiang, Lin, Yuping
Format: Journal Article
Language:English
Published: Switzerland MDPI AG 30.03.2023
MDPI
Subjects:
20th century
Academic achievement
Academic Performance
Benchmarking
Chronic fatigue syndrome
Classification
CNN
College students
Coronaviruses
COVID-19
Deep learning
Education
Health aspects
Humans
joint facial representation
Machine learning
Medical research
Metal fatigue
Mouth
MPA
Myopia
Neural networks
Observational studies
online fatigue detection
Pandemics
Questionnaires
Research methodology
Students
Surveys and Questionnaires
video-based online fatigue detection
United States
China
New Jersey
Germany
CNN
joint facial representation
online fatigue detection
video-based online fatigue detection
MPA
ISSN:1424-8220, 1424-8220
Online Access:Get full text
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Summary:Online fatigue estimation is, inevitably, in demand as fatigue can impair the health of college students and lower the quality of higher education. Therefore, it is essential to monitor college students’ fatigue to diminish its adverse effects on the health and academic performance of college students. However, former studies on student fatigue monitoring are mainly survey-based with offline analysis, instead of using constant fatigue monitoring. Hence, we proposed an explainable student fatigue estimation model based on joint facial representation. This model includes two modules: a spacial–temporal symptom classification module and a data-experience joint status inferring module. The first module tracks a student’s face and generates spatial–temporal features using a deep convolutional neural network (CNN) for the relevant drivers of abnormal symptom classification; the second module infers a student’s status with symptom classification results with maximum a posteriori (MAP) under the data-experience joint constraints. The model was trained on the benchmark NTHU Driver Drowsiness Detection (NTHU-DDD) dataset and tested on an Online Student Fatigue Monitoring (OSFM) dataset. Our method outperformed the other methods with an accuracy rate of 94.47% under the same training–testing setting. The results were significant for real-time monitoring of students’ fatigue states during online classes and could also provide practical strategies for in-person education.
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These authors contributed equally to this work.
ISSN:1424-8220
1424-8220
DOI:10.3390/s23073602