Traffic Sign Recognition Evaluation for Senior Adults Using EEG Signals
Elderly people are not likely to recognize road signs due to low cognitive ability and presbyopia. In our study, three shapes of traffic symbols (circles, squares, and triangles) which are most commonly used in road driving were used to evaluate the elderly drivers’ recognition. When traffic signs a...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-07-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/21/13/4607 |
| _version_ | 1797527898186317824 |
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| author | Dong-Woo Koh Jin-Kook Kwon Sang-Goog Lee |
| author_facet | Dong-Woo Koh Jin-Kook Kwon Sang-Goog Lee |
| author_sort | Dong-Woo Koh |
| collection | DOAJ |
| description | Elderly people are not likely to recognize road signs due to low cognitive ability and presbyopia. In our study, three shapes of traffic symbols (circles, squares, and triangles) which are most commonly used in road driving were used to evaluate the elderly drivers’ recognition. When traffic signs are randomly shown in HUD (head-up display), subjects compare them with the symbol displayed outside of the vehicle. In this test, we conducted a Go/Nogo test and determined the differences in ERP (event-related potential) data between correct and incorrect answers of EEG signals. As a result, the wrong answer rate for the elderly was 1.5 times higher than for the youths. All generation groups had a delay of 20–30 ms of P300 with incorrect answers. In order to achieve clearer differentiation, ERP data were modeled with unsupervised machine learning and supervised deep learning. The young group’s correct/incorrect data were classified well using unsupervised machine learning with no pre-processing, but the elderly group’s data were not. On the other hand, the elderly group’s data were classified with a high accuracy of 75% using supervised deep learning with simple signal processing. Our results can be used as a basis for the implementation of a personalized safe driving system for the elderly. |
| first_indexed | 2024-03-10T09:50:23Z |
| format | Article |
| id | doaj.art-efb6d5ac0a8a469b85f2db5f91d1efec |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T09:50:23Z |
| publishDate | 2021-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-efb6d5ac0a8a469b85f2db5f91d1efec2023-11-22T02:52:09ZengMDPI AGSensors1424-82202021-07-012113460710.3390/s21134607Traffic Sign Recognition Evaluation for Senior Adults Using EEG SignalsDong-Woo Koh0Jin-Kook Kwon1Sang-Goog Lee2Department of Media Engineering, Catholic University of Korea, 43 Jibong-ro, Bucheon-si 14662, KoreaCookingMind Cop. 23 Seocho-daero 74-gil, Seocho-gu, Seoul 06621, KoreaDepartment of Media Engineering, Catholic University of Korea, 43 Jibong-ro, Bucheon-si 14662, KoreaElderly people are not likely to recognize road signs due to low cognitive ability and presbyopia. In our study, three shapes of traffic symbols (circles, squares, and triangles) which are most commonly used in road driving were used to evaluate the elderly drivers’ recognition. When traffic signs are randomly shown in HUD (head-up display), subjects compare them with the symbol displayed outside of the vehicle. In this test, we conducted a Go/Nogo test and determined the differences in ERP (event-related potential) data between correct and incorrect answers of EEG signals. As a result, the wrong answer rate for the elderly was 1.5 times higher than for the youths. All generation groups had a delay of 20–30 ms of P300 with incorrect answers. In order to achieve clearer differentiation, ERP data were modeled with unsupervised machine learning and supervised deep learning. The young group’s correct/incorrect data were classified well using unsupervised machine learning with no pre-processing, but the elderly group’s data were not. On the other hand, the elderly group’s data were classified with a high accuracy of 75% using supervised deep learning with simple signal processing. Our results can be used as a basis for the implementation of a personalized safe driving system for the elderly.https://www.mdpi.com/1424-8220/21/13/4607traffic sign recognitionelectroencephalogrambrain computer interfaceelderly drivers |
| spellingShingle | Dong-Woo Koh Jin-Kook Kwon Sang-Goog Lee Traffic Sign Recognition Evaluation for Senior Adults Using EEG Signals Sensors traffic sign recognition electroencephalogram brain computer interface elderly drivers |
| title | Traffic Sign Recognition Evaluation for Senior Adults Using EEG Signals |
| title_full | Traffic Sign Recognition Evaluation for Senior Adults Using EEG Signals |
| title_fullStr | Traffic Sign Recognition Evaluation for Senior Adults Using EEG Signals |
| title_full_unstemmed | Traffic Sign Recognition Evaluation for Senior Adults Using EEG Signals |
| title_short | Traffic Sign Recognition Evaluation for Senior Adults Using EEG Signals |
| title_sort | traffic sign recognition evaluation for senior adults using eeg signals |
| topic | traffic sign recognition electroencephalogram brain computer interface elderly drivers |
| url | https://www.mdpi.com/1424-8220/21/13/4607 |
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