µ¥ÀÌÅͺ£À̽º ¿¬±¸È¸Áö(SIGDB)
ÇѱÛÁ¦¸ñ(Korean Title) |
À̹ÌÁö¿Í PPG µ¥ÀÌÅ͸¦ »ç¿ëÇÑ ¸ÖƼ¸ð´Þ µö·¯´× ±â¹ÝÀÇ ¿îÀüÀÚ Á¹À½ °¨Áö ¸ðµ¨ |
¿µ¹®Á¦¸ñ(English Title) |
Driver Drowsiness Detection Model using Image and PPG data Based on Multimodal Deep Learning |
ÀúÀÚ(Author) |
ÃÖÇüŹ
¹é¹®±â
°Àç½Ä
À±½Â¿ø
À̱Ôö
Hyung-Tak Choi
Moon-Ki Back
Jae-Sik Kang
Seung-Won Yoon
Kyu-Chul Lee
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¿ø¹®¼ö·Ïó(Citation) |
VOL 34 NO. 03 PP. 0045 ~ 0057 (2018. 12) |
Çѱ۳»¿ë (Korean Abstract) |
ÁÖÇà Áß¿¡ ¹ß»ýÇÏ´Â Á¹À½Àº Å« »ç°í·Î Á÷°áµÉ ¼ö ÀÖ´Â ¸Å¿ì À§ÇèÇÑ ¿îÀüÀÚ »óÅÂÀÌ´Ù. Á¹À½À» ¹æÁöÇϱâ À§ÇÏ¿© ¿îÀüÀÚÀÇ »óŸ¦ ÆľÇÇÏ´Â ÀüÅëÀûÀÎ Á¹À½ °¨Áö ¹æ¹ýµéÀÌ Á¸ÀçÇÏÁö¸¸ ¿îÀüÀÚµéÀÌ °¡Áö´Â °³°³ÀÎÀÇ Æ¯¼ºÀ» ¸ðµÎ ¹Ý¿µÇÑ ÀϹÝÈ µÈ ¿îÀüÀÚ »óÅ ÀνĿ¡´Â ÇÑ°è°¡ ÀÖ´Ù. ÃÖ±Ù¿¡´Â ¿îÀüÀÚÀÇ »óŸ¦ ÀνÄÇϱâ À§ÇÑ µö ·¯´× ±â¹ÝÀÇ »óÅÂÀÎ½Ä ¿¬±¸µéÀÌ Á¦¾ÈµÇ¾ú´Ù. µö ·¯´×Àº Àΰ£ÀÌ ¾Æ´Ñ ±â°è°¡ Ư¡À» ÃßÃâÇÏ¿© º¸´Ù ÀϹÝÈµÈ Àνĸðµ¨À» µµÃâÇÒ ¼ö ÀÖ´Â ÀåÁ¡ÀÌ ÀÖ´Ù. º» ¿¬±¸¿¡¼´Â ¿îÀüÀÚÀÇ »óŸ¦ ÆľÇÇϱâ À§ÇØ À̹ÌÁö¿Í PPG¸¦ µ¿½Ã¿¡ ÇнÀÇÏ¿© ±âÁ¸ µö ·¯´× ¹æ½Äº¸´Ù Á¤È®ÇÑ »óÅ ÀÎ½Ä ¸ðµ¨À» Á¦¾ÈÇÑ´Ù. º» ³í¹®Àº ¿îÀüÀÚÀÇ À̹ÌÁö¿Í PPG µ¥ÀÌÅÍ°¡ Á¹À½ °¨Áö¿¡ ¾î¶² ¿µÇâÀ» ¹ÌÄ¡´ÂÁö, ÇÔ²² »ç¿ëµÇ¾úÀ» ¶§ ÇнÀ ¸ðµ¨ÀÇ ¼º´ÉÀ» Çâ»ó½ÃÅ°´ÂÁö ½ÇÇèÀ» ÅëÇØÈ®ÀÎÇÏ¿´´Ù. À̹ÌÁö¸¸À» »ç¿ëÇßÀ» ¶§ º¸´Ù À̹ÌÁö¿Í PPG¸¦ ÇÔ²² »ç¿ëÇÏ¿´À» ¶§ 3%³»¿ÜÀÇ Á¤È®µµ Çâ»óÀ» È®ÀÎÇß´Ù. ¶ÇÇÑ, ¿îÀüÀÚÀÇ »óŸ¦ ¼¼ °¡Áö·Î ºÐ·ùÇÏ´Â ¸ÖƼ¸ð´Þ µö ·¯´× ±â¹ÝÀÇ ¸ðµ¨À» 96%ÀÇ ºÐ·ù Á¤È®µµ¸¦ º¸¿´´Ù.
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¿µ¹®³»¿ë (English Abstract) |
The drowsiness that occurs in the driving is a very dangerous driver condition that can be directly linked to a major accident. In order to prevent drowsiness, there are traditional drowsiness detection methods to grasp the driver 's condition, but there is a limit to the generalized driver' s condition recognition that reflects the individual characteristics of drivers. In recent years, deep learning based state recognition studies have been proposed to recognize drivers' condition. Deep learning has the advantage of extracting features from a non-human machine and deriving a more generalized recognition model. In this study, we propose a more accurate state recognition model than the existing deep learning method by learning image and PPG at the same time to grasp driver 's condition. This paper confirms the effect of driver 's image and PPG data on drowsiness detection and experiment to see if it improves the performance of learning model when used together. We confirmed the accuracy improvement of around 3% when using image and PPG together than using image alone. In addition, the multimodal deep learning based model that classifies the driver 's condition into three categories showed a classification accuracy of 96%.
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Å°¿öµå(Keyword) |
¿îÀüÀÚ Á¹À½ °¨Áö
ºÐ·ù
µö ·¯´×
¸ÖƼ¸ð´Þ
driver drowsiness detection
classification
deep learning
multimodal
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ÆÄÀÏ÷ºÎ |
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