Drowsiness Detection Based on Intelligent Systems with Nonlinear Features for Optimal Placement of Encephalogram Electrodes on the Cerebral Area
Drowsiness while driving can lead to accidents that are related to the loss of perception during emergencies that harm the health. Among physiological signals, brain waves have been used as informative signals for the analyses of behavioral observations, steering information, and other biosignals du...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-02-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/21/4/1255 |
| _version_ | 1797411857021009920 |
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| author | Seunghyeok Hong Hyun Jae Baek |
| author_facet | Seunghyeok Hong Hyun Jae Baek |
| author_sort | Seunghyeok Hong |
| collection | DOAJ |
| description | Drowsiness while driving can lead to accidents that are related to the loss of perception during emergencies that harm the health. Among physiological signals, brain waves have been used as informative signals for the analyses of behavioral observations, steering information, and other biosignals during drowsiness. We inspected the machine learning methods for drowsiness detection based on brain signals with varying quantities of information. The results demonstrated that machine learning could be utilized to compensate for a lack of information and to account for individual differences. Cerebral area selection approaches to decide optimal measurement locations could be utilized to minimize the discomfort of participants. Although other statistics could provide additional information in further study, the optimized machine learning method could prevent the dangers of drowsiness while driving by considering a transitional state with nonlinear features. Because brain signals can be altered not only by mental fatigue but also by health status, the optimization analysis of the system hardware and software will be able to increase the power-efficiency and accessibility in acquiring brain waves for health enhancements in daily life. |
| first_indexed | 2024-03-09T04:52:21Z |
| format | Article |
| id | doaj.art-d03f87f9467043c2af3d131ad38cafad |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T04:52:21Z |
| publishDate | 2021-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-d03f87f9467043c2af3d131ad38cafad2023-12-03T13:09:29ZengMDPI AGSensors1424-82202021-02-01214125510.3390/s21041255Drowsiness Detection Based on Intelligent Systems with Nonlinear Features for Optimal Placement of Encephalogram Electrodes on the Cerebral AreaSeunghyeok Hong0Hyun Jae Baek1Division of Data Science, The University of Suwon, Hwaseong-si 18323, KoreaDepartment of Medical and Mechatronics Engineering, Soonchunhyang University, Asan 31538, KoreaDrowsiness while driving can lead to accidents that are related to the loss of perception during emergencies that harm the health. Among physiological signals, brain waves have been used as informative signals for the analyses of behavioral observations, steering information, and other biosignals during drowsiness. We inspected the machine learning methods for drowsiness detection based on brain signals with varying quantities of information. The results demonstrated that machine learning could be utilized to compensate for a lack of information and to account for individual differences. Cerebral area selection approaches to decide optimal measurement locations could be utilized to minimize the discomfort of participants. Although other statistics could provide additional information in further study, the optimized machine learning method could prevent the dangers of drowsiness while driving by considering a transitional state with nonlinear features. Because brain signals can be altered not only by mental fatigue but also by health status, the optimization analysis of the system hardware and software will be able to increase the power-efficiency and accessibility in acquiring brain waves for health enhancements in daily life.https://www.mdpi.com/1424-8220/21/4/1255EEGbiosignalmeasurementsleepinessfatigueDDS |
| spellingShingle | Seunghyeok Hong Hyun Jae Baek Drowsiness Detection Based on Intelligent Systems with Nonlinear Features for Optimal Placement of Encephalogram Electrodes on the Cerebral Area Sensors EEG biosignal measurement sleepiness fatigue DDS |
| title | Drowsiness Detection Based on Intelligent Systems with Nonlinear Features for Optimal Placement of Encephalogram Electrodes on the Cerebral Area |
| title_full | Drowsiness Detection Based on Intelligent Systems with Nonlinear Features for Optimal Placement of Encephalogram Electrodes on the Cerebral Area |
| title_fullStr | Drowsiness Detection Based on Intelligent Systems with Nonlinear Features for Optimal Placement of Encephalogram Electrodes on the Cerebral Area |
| title_full_unstemmed | Drowsiness Detection Based on Intelligent Systems with Nonlinear Features for Optimal Placement of Encephalogram Electrodes on the Cerebral Area |
| title_short | Drowsiness Detection Based on Intelligent Systems with Nonlinear Features for Optimal Placement of Encephalogram Electrodes on the Cerebral Area |
| title_sort | drowsiness detection based on intelligent systems with nonlinear features for optimal placement of encephalogram electrodes on the cerebral area |
| topic | EEG biosignal measurement sleepiness fatigue DDS |
| url | https://www.mdpi.com/1424-8220/21/4/1255 |
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