• Journal of Korea Multimedia Society
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• v.24 no.8
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• pp.1149-1160
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• 2021

Lack of sleep time increases risks of fatigue, hypomnesis, decreased emotional stability, indigestion, and dementia. The risks can be reduced by providing eyelid-warming, inducing sleep and improving sleep quality. However, effective warming temperature to an person varies depending on physical condition and the individual. The various types of frequencies can be identified in brain wave from a person and amount of frequencies is also changed continuously before and after sleep. Therefore we can identify the user's sleep stage with brain wave, namely EEG. Effective sleep induction is possible if warming temperature to a person is controlled based on EEG. In this paper, we propose customized warming control techniques based on EEG for a efficient and effective sleep induction. As an experiment, sleep induction effects of standard sleep mask and customized temperature control techniques sleep mask are compared. EEG data and warming temperature were measured in 100 experiments. At customized warming control techniques, experiments showed that the ratio of alpha and theta waves increased by 3.21%p and the time to sleep decreased by 85 seconds. It will contribute to effective sleep induction and performance verification methods in customized sleep mask systems.

• Journal of Convergence for Information Technology
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• v.9 no.12
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• pp.8-16
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• 2019

Customized sleep care service needs to be provided differently for individuals since individual has different degree of sleep disorder. Because the brainwave data shows unique waveform characteristics for each person, this characteristic can be used to identify individuals. Personal identification provides an important role in enabling customized services. When you blink, you can obtain brain wave characteristics by measuring the area of the frontal lobe. Therefore, a real-time personal identification algorithm based on blinking EOG for customized sleep care service is proposed in this paper. For evaluation, 10 individuals were tested for personal identification accuracy. The results of the experiment confirmed that a maximum accuracy of 93% were taken. Algorithms can be developed by reflecting characteristics such as changes in the external environment in the future.