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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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
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Abstract	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.
AbstractList	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.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. 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.
Audience	Academic
Author	Tian, Zhiqiang Lin, Yuping Lin, Jiaqin Li, Xiaomian
AuthorAffiliation	2 Institute of Artificial Intelligence and Robotics, School of Electronic and Information Engineering, Xi’an Jiaotong University, Xi’an 710049, China; ljq0306@stu.xjtu.edu.cn 3 School of Software Engineering, Xi’an Jiaotong University, Xi’an 710049, China; zhiqiangtian@xjtu.edu.cn 1 School of Foreign Studies, Xi’an Jiaotong University, Xi’an 710049, China; mianmianli@126.com
AuthorAffiliation_xml	– name: 1 School of Foreign Studies, Xi’an Jiaotong University, Xi’an 710049, China; mianmianli@126.com – name: 2 Institute of Artificial Intelligence and Robotics, School of Electronic and Information Engineering, Xi’an Jiaotong University, Xi’an 710049, China; ljq0306@stu.xjtu.edu.cn – name: 3 School of Software Engineering, Xi’an Jiaotong University, Xi’an 710049, China; zhiqiangtian@xjtu.edu.cn
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Cites_doi	10.1088/1757-899X/912/6/062066 10.1109/ACCESS.2020.3011028 10.1037/0033-2909.127.1.87 10.14479/jkoos.2021.26.1.73 10.1111/ppc.12765 10.3390/app12042224 10.1097/OPX.0b013e31812f5f51 10.1109/CVPR.2016.90 10.4103/ijo.IJO_2981_20 10.1145/1102351.1102418 10.1109/VECIMS.2011.6053857 10.1080/08964280903231979 10.1109/IFOST.2010.5667915 10.1109/IVS.2011.5940406 10.21449/ijate.788078 10.1589/jpts.28.1813 10.1109/WoSSPA.2013.6602353 10.7899/JCE-15-27 10.1589/jpts.28.1233 10.1016/S0142-1123(03)00051-3 10.1007/s00521-020-05209-7 10.1111/bjhp.12485 10.1620/tjem.231.37 10.1007/978-3-319-65217-7 10.1080/23267224.1899.10649736 10.3200/JACH.57.2.150-158 10.3390/s151229822 10.1016/S0140-6736(02)30453-7 10.1109/ICCV.2017.74 10.3390/su13084468 10.1038/s41467-022-32507-6 10.1002/j.2161-1882.2009.tb00109.x 10.1109/CVPR.2009.5206848 10.1034/j.1600-0420.2000.078001026.x 10.1007/978-3-319-54526-4_9 10.1016/j.jneuroling.2020.100941 10.4324/9780203995549 10.32398/cjhp.v5i2.1240 10.1007/978-3-319-46484-8_2 10.1111/aos.13885 10.1016/j.nut.2008.05.003
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SubjectTerms	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
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Title	An Explainable Student Fatigue Monitoring Module with Joint Facial Representation
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