A PERCLOS Method for Fine Characterization of Behaviour Circadian Rhythm
Objective To establish a measurement method of the percentage of eyelid closure over the pupil over time (PERCLOS) to finely characterize behaviour circadian rhythm. Methods A computer program was designed based on multitask convolutional neural network f
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Abstract Objective To establish a measurement method of the percentage of eyelid closure over the pupil over time (PERCLOS) to finely characterize behaviour circadian rhythm. Methods A computer program was designed based on multitask convolutional neural network for the treatment of videos to get PERCLOS quantitative values. 7 volunteers were recruited in this research. The volunteers were asked to face the personal computers and play a simple game for 5 min, doing the test 4 times a day that was just after getting up in the morning, at noon, in the evening and just before going to bed. Their videos were recorded and treated with the computer program to obtain PERCLOS results. The results showed that the PERCLOS values of 6 young persons increased from morning to night in accordance with the circadian rhythm of youth, while an elder female volunteer showed a different circadian rhythm from that of the youth. Conclusions The PERCLOS method for characterization of behaviour circadian rhythm was successfully developed, which would serve as effective tool for circadian rhythm related studies.
Y. Gu · Z. Chen · J. Zhang (B) · P. Ding · W. Deng The Second Research Institute of Civil Aviation Administration of China, 610041 Chengdu, China e-mail: [email protected] Z. Chen e-mail: [email protected] Y. Gu · W. Deng Civil Aviation Flight, University of China, 618307 Guanghan, China Z. Chen Civil Aviation Medicine Center, Civil Aviation Administration of China (Civil Aviation Hospital), 100123 Beijing, China G. Zou Air Traffic Management Regulation Office, Civil Aviation Administration of China, 10071 Beijing, China P. Ding College of Computer Science, Sichuan University, 610056 Chengdu, China © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 S. Long and B. S. Dhillon (eds.), Man-Machine-Environment System Engineering, Lecture Notes in Electrical Engineering 645, https://doi.org/10.1007/978-981-15-6978-4_30
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Keywords Percentage of eyelid closure over the pupil over time · Circadian rhythm · Multitask convolutional neural network
1 Introduction Circadian rhythm exists in human body and influences people’s life and production. The discovery of the molecular mechanism for circadian rhythm in organisms won the Nobel Prize in 2017 [1]. Circadian rhythm molecular mechanism controls body’s nervous systems and influences body behaviours, further affecting people’s life and production, such as to keep health, to work at sunrise and sleep at sunset and to lead to fatigue and further decreasing productivity, threatening production safety. The circadian rhythms of individuals are regulated by endogenous (e.g., circadian pacemaker, peripheral oscillators, clock genes) and exogenous factors (e.g., light, feeding, age, social behaviour, work and schedules) and are different from each other [2, 3]. Therefore, it is necessary to establish simple and objective methods to learn individual behaviour circadian rhythm more precisely and accurate
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