Modeling and Recognition of Driving Fatigue State Based on R-R Intervals of ECG Data

Driving fatigue is an important contributing factor to traffic crashes. Developing a system that monitors the driver's fatigue level in real time and produces alarm signals when necessary, is important for the prevention of accidents. In the past decades, many recognition algorithms were develo...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Linhong Wang [verfasserIn] Jingwei Li [verfasserIn] Yunhao Wang [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2019

Schlagwörter:	Driving fatigue recognition model R-R intervals conditional variance

Übergeordnetes Werk:	In: IEEE Access - IEEE, 2014, 7(2019), Seite 175584-175593
Übergeordnetes Werk:	volume:7 ; year:2019 ; pages:175584-175593

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1109/ACCESS.2019.2956652

Katalog-ID:	DOAJ048632392

Internformat


LEADER	01000caa a22002652 4500
001	DOAJ048632392
003	DE-627
005	20230501191257.0
007	cr uuu---uuuuu
008	230227s2019 xx \|\|\|\|\|o 00\| \|\|eng c
024	7		\|a 10.1109/ACCESS.2019.2956652 \|2 doi
035			\|a (DE-627)DOAJ048632392
035			\|a (DE-599)DOAJ81a0746f145d4475b4252e62fb9a2272
040			\|a DE-627 \|b ger \|c DE-627 \|e rakwb
041			\|a eng
050		0	\|a TK1-9971
100	0		\|a Linhong Wang \|e verfasserin \|4 aut
245	1	0	\|a Modeling and Recognition of Driving Fatigue State Based on R-R Intervals of ECG Data
264		1	\|c 2019
336			\|a Text \|b txt \|2 rdacontent
337			\|a Computermedien \|b c \|2 rdamedia
338			\|a Online-Ressource \|b cr \|2 rdacarrier
520			\|a Driving fatigue is an important contributing factor to traffic crashes. Developing a system that monitors the driver's fatigue level in real time and produces alarm signals when necessary, is important for the prevention of accidents. In the past decades, many recognition algorithms were developed based on multiple indicators. However, the relationship between R-R intervals of ECG and driving fatigue has not been studied. We develop a model to recognize the driving fatigue state based on R-R intervals. The cluster effect in the R-R interval sequence is found based on the stationary test and ARCH effect test. Then the AR (1)-GARCH (1, 1) model is developed to fit the time sequence of R-R intervals. The conditional variance of the residual R-R sequence is used to recognize whether there are changes in driving states. Field data was collected on the freeway between Baicheng and Changchun cities, where 10 drivers took part in the experiments. Validations are conducted to test the effectiveness of the developed model, and the results show that the recognitions of driving fatigue for the 10 drivers are correct in all cases. In addition, the recognition time delay is smaller than 5 minutes.
650		4	\|a Driving fatigue
650		4	\|a recognition model
650		4	\|a R-R intervals
650		4	\|a conditional variance
653		0	\|a Electrical engineering. Electronics. Nuclear engineering
700	0		\|a Jingwei Li \|e verfasserin \|4 aut
700	0		\|a Yunhao Wang \|e verfasserin \|4 aut
773	0	8	\|i In \|t IEEE Access \|d IEEE, 2014 \|g 7(2019), Seite 175584-175593 \|w (DE-627)728440385 \|w (DE-600)2687964-5 \|x 21693536 \|7 nnns
773	1	8	\|g volume:7 \|g year:2019 \|g pages:175584-175593
856	4	0	\|u https://doi.org/10.1109/ACCESS.2019.2956652 \|z kostenfrei
856	4	0	\|u https://doaj.org/article/81a0746f145d4475b4252e62fb9a2272 \|z kostenfrei
856	4	0	\|u https://ieeexplore.ieee.org/document/8917590/ \|z kostenfrei
856	4	2	\|u https://doaj.org/toc/2169-3536 \|y Journal toc \|z kostenfrei
912			\|a GBV_USEFLAG_A
912			\|a SYSFLAG_A
912			\|a GBV_DOAJ
912			\|a SSG-OLC-PHA
912			\|a GBV_ILN_11
912			\|a GBV_ILN_20
912			\|a GBV_ILN_22
912			\|a GBV_ILN_23
912			\|a GBV_ILN_24
912			\|a GBV_ILN_31
912			\|a GBV_ILN_39
912			\|a GBV_ILN_40
912			\|a GBV_ILN_60
912			\|a GBV_ILN_62
912			\|a GBV_ILN_63
912			\|a GBV_ILN_65
912			\|a GBV_ILN_69
912			\|a GBV_ILN_70
912			\|a GBV_ILN_73
912			\|a GBV_ILN_95
912			\|a GBV_ILN_105
912			\|a GBV_ILN_110
912			\|a GBV_ILN_151
912			\|a GBV_ILN_161
912			\|a GBV_ILN_170
912			\|a GBV_ILN_213
912			\|a GBV_ILN_230
912			\|a GBV_ILN_285
912			\|a GBV_ILN_293
912			\|a GBV_ILN_370
912			\|a GBV_ILN_602
912			\|a GBV_ILN_2014
912			\|a GBV_ILN_4012
912			\|a GBV_ILN_4037
912			\|a GBV_ILN_4112
912			\|a GBV_ILN_4125
912			\|a GBV_ILN_4126
912			\|a GBV_ILN_4249
912			\|a GBV_ILN_4305
912			\|a GBV_ILN_4306
912			\|a GBV_ILN_4307
912			\|a GBV_ILN_4313
912			\|a GBV_ILN_4322
912			\|a GBV_ILN_4323
912			\|a GBV_ILN_4324
912			\|a GBV_ILN_4325
912			\|a GBV_ILN_4335
912			\|a GBV_ILN_4338
912			\|a GBV_ILN_4367
912			\|a GBV_ILN_4700
951			\|a AR
952			\|d 7 \|j 2019 \|h 175584-175593

Indexfelder

author_variant	l w lw j l jl y w yw
matchkey_str	article:21693536:2019----::oeignrcgiinfrvnftgettbsdn
hierarchy_sort_str	2019
callnumber-subject-code	TK
publishDate	2019
allfields	10.1109/ACCESS.2019.2956652 doi (DE-627)DOAJ048632392 (DE-599)DOAJ81a0746f145d4475b4252e62fb9a2272 DE-627 ger DE-627 rakwb eng TK1-9971 Linhong Wang verfasserin aut Modeling and Recognition of Driving Fatigue State Based on R-R Intervals of ECG Data 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Driving fatigue is an important contributing factor to traffic crashes. Developing a system that monitors the driver's fatigue level in real time and produces alarm signals when necessary, is important for the prevention of accidents. In the past decades, many recognition algorithms were developed based on multiple indicators. However, the relationship between R-R intervals of ECG and driving fatigue has not been studied. We develop a model to recognize the driving fatigue state based on R-R intervals. The cluster effect in the R-R interval sequence is found based on the stationary test and ARCH effect test. Then the AR (1)-GARCH (1, 1) model is developed to fit the time sequence of R-R intervals. The conditional variance of the residual R-R sequence is used to recognize whether there are changes in driving states. Field data was collected on the freeway between Baicheng and Changchun cities, where 10 drivers took part in the experiments. Validations are conducted to test the effectiveness of the developed model, and the results show that the recognitions of driving fatigue for the 10 drivers are correct in all cases. In addition, the recognition time delay is smaller than 5 minutes. Driving fatigue recognition model R-R intervals conditional variance Electrical engineering. Electronics. Nuclear engineering Jingwei Li verfasserin aut Yunhao Wang verfasserin aut In IEEE Access IEEE, 2014 7(2019), Seite 175584-175593 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:7 year:2019 pages:175584-175593 https://doi.org/10.1109/ACCESS.2019.2956652 kostenfrei https://doaj.org/article/81a0746f145d4475b4252e62fb9a2272 kostenfrei https://ieeexplore.ieee.org/document/8917590/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 7 2019 175584-175593
spelling	10.1109/ACCESS.2019.2956652 doi (DE-627)DOAJ048632392 (DE-599)DOAJ81a0746f145d4475b4252e62fb9a2272 DE-627 ger DE-627 rakwb eng TK1-9971 Linhong Wang verfasserin aut Modeling and Recognition of Driving Fatigue State Based on R-R Intervals of ECG Data 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Driving fatigue is an important contributing factor to traffic crashes. Developing a system that monitors the driver's fatigue level in real time and produces alarm signals when necessary, is important for the prevention of accidents. In the past decades, many recognition algorithms were developed based on multiple indicators. However, the relationship between R-R intervals of ECG and driving fatigue has not been studied. We develop a model to recognize the driving fatigue state based on R-R intervals. The cluster effect in the R-R interval sequence is found based on the stationary test and ARCH effect test. Then the AR (1)-GARCH (1, 1) model is developed to fit the time sequence of R-R intervals. The conditional variance of the residual R-R sequence is used to recognize whether there are changes in driving states. Field data was collected on the freeway between Baicheng and Changchun cities, where 10 drivers took part in the experiments. Validations are conducted to test the effectiveness of the developed model, and the results show that the recognitions of driving fatigue for the 10 drivers are correct in all cases. In addition, the recognition time delay is smaller than 5 minutes. Driving fatigue recognition model R-R intervals conditional variance Electrical engineering. Electronics. Nuclear engineering Jingwei Li verfasserin aut Yunhao Wang verfasserin aut In IEEE Access IEEE, 2014 7(2019), Seite 175584-175593 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:7 year:2019 pages:175584-175593 https://doi.org/10.1109/ACCESS.2019.2956652 kostenfrei https://doaj.org/article/81a0746f145d4475b4252e62fb9a2272 kostenfrei https://ieeexplore.ieee.org/document/8917590/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 7 2019 175584-175593
allfields_unstemmed	10.1109/ACCESS.2019.2956652 doi (DE-627)DOAJ048632392 (DE-599)DOAJ81a0746f145d4475b4252e62fb9a2272 DE-627 ger DE-627 rakwb eng TK1-9971 Linhong Wang verfasserin aut Modeling and Recognition of Driving Fatigue State Based on R-R Intervals of ECG Data 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Driving fatigue is an important contributing factor to traffic crashes. Developing a system that monitors the driver's fatigue level in real time and produces alarm signals when necessary, is important for the prevention of accidents. In the past decades, many recognition algorithms were developed based on multiple indicators. However, the relationship between R-R intervals of ECG and driving fatigue has not been studied. We develop a model to recognize the driving fatigue state based on R-R intervals. The cluster effect in the R-R interval sequence is found based on the stationary test and ARCH effect test. Then the AR (1)-GARCH (1, 1) model is developed to fit the time sequence of R-R intervals. The conditional variance of the residual R-R sequence is used to recognize whether there are changes in driving states. Field data was collected on the freeway between Baicheng and Changchun cities, where 10 drivers took part in the experiments. Validations are conducted to test the effectiveness of the developed model, and the results show that the recognitions of driving fatigue for the 10 drivers are correct in all cases. In addition, the recognition time delay is smaller than 5 minutes. Driving fatigue recognition model R-R intervals conditional variance Electrical engineering. Electronics. Nuclear engineering Jingwei Li verfasserin aut Yunhao Wang verfasserin aut In IEEE Access IEEE, 2014 7(2019), Seite 175584-175593 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:7 year:2019 pages:175584-175593 https://doi.org/10.1109/ACCESS.2019.2956652 kostenfrei https://doaj.org/article/81a0746f145d4475b4252e62fb9a2272 kostenfrei https://ieeexplore.ieee.org/document/8917590/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 7 2019 175584-175593
allfieldsGer	10.1109/ACCESS.2019.2956652 doi (DE-627)DOAJ048632392 (DE-599)DOAJ81a0746f145d4475b4252e62fb9a2272 DE-627 ger DE-627 rakwb eng TK1-9971 Linhong Wang verfasserin aut Modeling and Recognition of Driving Fatigue State Based on R-R Intervals of ECG Data 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Driving fatigue is an important contributing factor to traffic crashes. Developing a system that monitors the driver's fatigue level in real time and produces alarm signals when necessary, is important for the prevention of accidents. In the past decades, many recognition algorithms were developed based on multiple indicators. However, the relationship between R-R intervals of ECG and driving fatigue has not been studied. We develop a model to recognize the driving fatigue state based on R-R intervals. The cluster effect in the R-R interval sequence is found based on the stationary test and ARCH effect test. Then the AR (1)-GARCH (1, 1) model is developed to fit the time sequence of R-R intervals. The conditional variance of the residual R-R sequence is used to recognize whether there are changes in driving states. Field data was collected on the freeway between Baicheng and Changchun cities, where 10 drivers took part in the experiments. Validations are conducted to test the effectiveness of the developed model, and the results show that the recognitions of driving fatigue for the 10 drivers are correct in all cases. In addition, the recognition time delay is smaller than 5 minutes. Driving fatigue recognition model R-R intervals conditional variance Electrical engineering. Electronics. Nuclear engineering Jingwei Li verfasserin aut Yunhao Wang verfasserin aut In IEEE Access IEEE, 2014 7(2019), Seite 175584-175593 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:7 year:2019 pages:175584-175593 https://doi.org/10.1109/ACCESS.2019.2956652 kostenfrei https://doaj.org/article/81a0746f145d4475b4252e62fb9a2272 kostenfrei https://ieeexplore.ieee.org/document/8917590/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 7 2019 175584-175593
allfieldsSound	10.1109/ACCESS.2019.2956652 doi (DE-627)DOAJ048632392 (DE-599)DOAJ81a0746f145d4475b4252e62fb9a2272 DE-627 ger DE-627 rakwb eng TK1-9971 Linhong Wang verfasserin aut Modeling and Recognition of Driving Fatigue State Based on R-R Intervals of ECG Data 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Driving fatigue is an important contributing factor to traffic crashes. Developing a system that monitors the driver's fatigue level in real time and produces alarm signals when necessary, is important for the prevention of accidents. In the past decades, many recognition algorithms were developed based on multiple indicators. However, the relationship between R-R intervals of ECG and driving fatigue has not been studied. We develop a model to recognize the driving fatigue state based on R-R intervals. The cluster effect in the R-R interval sequence is found based on the stationary test and ARCH effect test. Then the AR (1)-GARCH (1, 1) model is developed to fit the time sequence of R-R intervals. The conditional variance of the residual R-R sequence is used to recognize whether there are changes in driving states. Field data was collected on the freeway between Baicheng and Changchun cities, where 10 drivers took part in the experiments. Validations are conducted to test the effectiveness of the developed model, and the results show that the recognitions of driving fatigue for the 10 drivers are correct in all cases. In addition, the recognition time delay is smaller than 5 minutes. Driving fatigue recognition model R-R intervals conditional variance Electrical engineering. Electronics. Nuclear engineering Jingwei Li verfasserin aut Yunhao Wang verfasserin aut In IEEE Access IEEE, 2014 7(2019), Seite 175584-175593 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:7 year:2019 pages:175584-175593 https://doi.org/10.1109/ACCESS.2019.2956652 kostenfrei https://doaj.org/article/81a0746f145d4475b4252e62fb9a2272 kostenfrei https://ieeexplore.ieee.org/document/8917590/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 7 2019 175584-175593
language	English
source	In IEEE Access 7(2019), Seite 175584-175593 volume:7 year:2019 pages:175584-175593
sourceStr	In IEEE Access 7(2019), Seite 175584-175593 volume:7 year:2019 pages:175584-175593
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Driving fatigue recognition model R-R intervals conditional variance Electrical engineering. Electronics. Nuclear engineering
isfreeaccess_bool	true
container_title	IEEE Access
authorswithroles_txt_mv	Linhong Wang @@aut@@ Jingwei Li @@aut@@ Yunhao Wang @@aut@@
publishDateDaySort_date	2019-01-01T00:00:00Z
hierarchy_top_id	728440385
id	DOAJ048632392
language_de	englisch
fullrecord	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">DOAJ048632392</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230501191257.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">230227s2019 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1109/ACCESS.2019.2956652</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)DOAJ048632392</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)DOAJ81a0746f145d4475b4252e62fb9a2272</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="050" ind1=" " ind2="0"><subfield code="a">TK1-9971</subfield></datafield><datafield tag="100" ind1="0" ind2=" "><subfield code="a">Linhong Wang</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Modeling and Recognition of Driving Fatigue State Based on R-R Intervals of ECG Data</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2019</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Driving fatigue is an important contributing factor to traffic crashes. Developing a system that monitors the driver's fatigue level in real time and produces alarm signals when necessary, is important for the prevention of accidents. In the past decades, many recognition algorithms were developed based on multiple indicators. However, the relationship between R-R intervals of ECG and driving fatigue has not been studied. We develop a model to recognize the driving fatigue state based on R-R intervals. The cluster effect in the R-R interval sequence is found based on the stationary test and ARCH effect test. Then the AR (1)-GARCH (1, 1) model is developed to fit the time sequence of R-R intervals. The conditional variance of the residual R-R sequence is used to recognize whether there are changes in driving states. Field data was collected on the freeway between Baicheng and Changchun cities, where 10 drivers took part in the experiments. Validations are conducted to test the effectiveness of the developed model, and the results show that the recognitions of driving fatigue for the 10 drivers are correct in all cases. In addition, the recognition time delay is smaller than 5 minutes.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Driving fatigue</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">recognition model</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">R-R intervals</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">conditional variance</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Electrical engineering. Electronics. Nuclear engineering</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Jingwei Li</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Yunhao Wang</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">In</subfield><subfield code="t">IEEE Access</subfield><subfield code="d">IEEE, 2014</subfield><subfield code="g">7(2019), Seite 175584-175593</subfield><subfield code="w">(DE-627)728440385</subfield><subfield code="w">(DE-600)2687964-5</subfield><subfield code="x">21693536</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:7</subfield><subfield code="g">year:2019</subfield><subfield code="g">pages:175584-175593</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.1109/ACCESS.2019.2956652</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doaj.org/article/81a0746f145d4475b4252e62fb9a2272</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://ieeexplore.ieee.org/document/8917590/</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="u">https://doaj.org/toc/2169-3536</subfield><subfield code="y">Journal toc</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_DOAJ</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-PHA</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_11</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_20</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_23</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_24</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_31</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_39</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_60</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_62</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_63</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_65</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_69</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_73</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_95</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_105</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_151</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_161</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_170</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_213</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_230</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_285</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_293</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_370</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_602</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2014</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4012</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4037</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4125</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4126</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4249</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4305</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4306</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4307</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4313</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4322</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4323</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4324</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4325</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4335</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4338</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4367</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4700</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">7</subfield><subfield code="j">2019</subfield><subfield code="h">175584-175593</subfield></datafield></record></collection>
callnumber-first	T - Technology
author	Linhong Wang
spellingShingle	Linhong Wang misc TK1-9971 misc Driving fatigue misc recognition model misc R-R intervals misc conditional variance misc Electrical engineering. Electronics. Nuclear engineering Modeling and Recognition of Driving Fatigue State Based on R-R Intervals of ECG Data
authorStr	Linhong Wang
ppnlink_with_tag_str_mv	@@773@@(DE-627)728440385
format	electronic Article
delete_txt_mv	keep
author_role	aut aut aut
collection	DOAJ
remote_str	true
callnumber-label	TK1-9971
illustrated	Not Illustrated
issn	21693536
topic_title	TK1-9971 Modeling and Recognition of Driving Fatigue State Based on R-R Intervals of ECG Data Driving fatigue recognition model R-R intervals conditional variance
topic	misc TK1-9971 misc Driving fatigue misc recognition model misc R-R intervals misc conditional variance misc Electrical engineering. Electronics. Nuclear engineering
topic_unstemmed	misc TK1-9971 misc Driving fatigue misc recognition model misc R-R intervals misc conditional variance misc Electrical engineering. Electronics. Nuclear engineering
topic_browse	misc TK1-9971 misc Driving fatigue misc recognition model misc R-R intervals misc conditional variance misc Electrical engineering. Electronics. Nuclear engineering
format_facet	Elektronische Aufsätze Aufsätze Elektronische Ressource
format_main_str_mv	Text Zeitschrift/Artikel
carriertype_str_mv	cr
hierarchy_parent_title	IEEE Access
hierarchy_parent_id	728440385
hierarchy_top_title	IEEE Access
isfreeaccess_txt	true
familylinks_str_mv	(DE-627)728440385 (DE-600)2687964-5
title	Modeling and Recognition of Driving Fatigue State Based on R-R Intervals of ECG Data
ctrlnum	(DE-627)DOAJ048632392 (DE-599)DOAJ81a0746f145d4475b4252e62fb9a2272
title_full	Modeling and Recognition of Driving Fatigue State Based on R-R Intervals of ECG Data
author_sort	Linhong Wang
journal	IEEE Access
journalStr	IEEE Access
callnumber-first-code	T
lang_code	eng
isOA_bool	true
recordtype	marc
publishDateSort	2019
contenttype_str_mv	txt
container_start_page	175584
author_browse	Linhong Wang Jingwei Li Yunhao Wang
container_volume	7
class	TK1-9971
format_se	Elektronische Aufsätze
author-letter	Linhong Wang
doi_str_mv	10.1109/ACCESS.2019.2956652
author2-role	verfasserin
title_sort	modeling and recognition of driving fatigue state based on r-r intervals of ecg data
callnumber	TK1-9971
title_auth	Modeling and Recognition of Driving Fatigue State Based on R-R Intervals of ECG Data
abstract	Driving fatigue is an important contributing factor to traffic crashes. Developing a system that monitors the driver's fatigue level in real time and produces alarm signals when necessary, is important for the prevention of accidents. In the past decades, many recognition algorithms were developed based on multiple indicators. However, the relationship between R-R intervals of ECG and driving fatigue has not been studied. We develop a model to recognize the driving fatigue state based on R-R intervals. The cluster effect in the R-R interval sequence is found based on the stationary test and ARCH effect test. Then the AR (1)-GARCH (1, 1) model is developed to fit the time sequence of R-R intervals. The conditional variance of the residual R-R sequence is used to recognize whether there are changes in driving states. Field data was collected on the freeway between Baicheng and Changchun cities, where 10 drivers took part in the experiments. Validations are conducted to test the effectiveness of the developed model, and the results show that the recognitions of driving fatigue for the 10 drivers are correct in all cases. In addition, the recognition time delay is smaller than 5 minutes.
abstractGer	Driving fatigue is an important contributing factor to traffic crashes. Developing a system that monitors the driver's fatigue level in real time and produces alarm signals when necessary, is important for the prevention of accidents. In the past decades, many recognition algorithms were developed based on multiple indicators. However, the relationship between R-R intervals of ECG and driving fatigue has not been studied. We develop a model to recognize the driving fatigue state based on R-R intervals. The cluster effect in the R-R interval sequence is found based on the stationary test and ARCH effect test. Then the AR (1)-GARCH (1, 1) model is developed to fit the time sequence of R-R intervals. The conditional variance of the residual R-R sequence is used to recognize whether there are changes in driving states. Field data was collected on the freeway between Baicheng and Changchun cities, where 10 drivers took part in the experiments. Validations are conducted to test the effectiveness of the developed model, and the results show that the recognitions of driving fatigue for the 10 drivers are correct in all cases. In addition, the recognition time delay is smaller than 5 minutes.
abstract_unstemmed	Driving fatigue is an important contributing factor to traffic crashes. Developing a system that monitors the driver's fatigue level in real time and produces alarm signals when necessary, is important for the prevention of accidents. In the past decades, many recognition algorithms were developed based on multiple indicators. However, the relationship between R-R intervals of ECG and driving fatigue has not been studied. We develop a model to recognize the driving fatigue state based on R-R intervals. The cluster effect in the R-R interval sequence is found based on the stationary test and ARCH effect test. Then the AR (1)-GARCH (1, 1) model is developed to fit the time sequence of R-R intervals. The conditional variance of the residual R-R sequence is used to recognize whether there are changes in driving states. Field data was collected on the freeway between Baicheng and Changchun cities, where 10 drivers took part in the experiments. Validations are conducted to test the effectiveness of the developed model, and the results show that the recognitions of driving fatigue for the 10 drivers are correct in all cases. In addition, the recognition time delay is smaller than 5 minutes.
collection_details	GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700
title_short	Modeling and Recognition of Driving Fatigue State Based on R-R Intervals of ECG Data
url	https://doi.org/10.1109/ACCESS.2019.2956652 https://doaj.org/article/81a0746f145d4475b4252e62fb9a2272 https://ieeexplore.ieee.org/document/8917590/ https://doaj.org/toc/2169-3536
remote_bool	true
author2	Jingwei Li Yunhao Wang
author2Str	Jingwei Li Yunhao Wang
ppnlink	728440385
callnumber-subject	TK - Electrical and Nuclear Engineering
mediatype_str_mv	c
isOA_txt	true
hochschulschrift_bool	false
doi_str	10.1109/ACCESS.2019.2956652
callnumber-a	TK1-9971
up_date	2024-07-03T18:51:21.041Z
_version_	1803584988557344768
fullrecord_marcxml	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">DOAJ048632392</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230501191257.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">230227s2019 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1109/ACCESS.2019.2956652</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)DOAJ048632392</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)DOAJ81a0746f145d4475b4252e62fb9a2272</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="050" ind1=" " ind2="0"><subfield code="a">TK1-9971</subfield></datafield><datafield tag="100" ind1="0" ind2=" "><subfield code="a">Linhong Wang</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Modeling and Recognition of Driving Fatigue State Based on R-R Intervals of ECG Data</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2019</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Driving fatigue is an important contributing factor to traffic crashes. Developing a system that monitors the driver's fatigue level in real time and produces alarm signals when necessary, is important for the prevention of accidents. In the past decades, many recognition algorithms were developed based on multiple indicators. However, the relationship between R-R intervals of ECG and driving fatigue has not been studied. We develop a model to recognize the driving fatigue state based on R-R intervals. The cluster effect in the R-R interval sequence is found based on the stationary test and ARCH effect test. Then the AR (1)-GARCH (1, 1) model is developed to fit the time sequence of R-R intervals. The conditional variance of the residual R-R sequence is used to recognize whether there are changes in driving states. Field data was collected on the freeway between Baicheng and Changchun cities, where 10 drivers took part in the experiments. Validations are conducted to test the effectiveness of the developed model, and the results show that the recognitions of driving fatigue for the 10 drivers are correct in all cases. In addition, the recognition time delay is smaller than 5 minutes.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Driving fatigue</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">recognition model</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">R-R intervals</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">conditional variance</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Electrical engineering. Electronics. Nuclear engineering</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Jingwei Li</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Yunhao Wang</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">In</subfield><subfield code="t">IEEE Access</subfield><subfield code="d">IEEE, 2014</subfield><subfield code="g">7(2019), Seite 175584-175593</subfield><subfield code="w">(DE-627)728440385</subfield><subfield code="w">(DE-600)2687964-5</subfield><subfield code="x">21693536</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:7</subfield><subfield code="g">year:2019</subfield><subfield code="g">pages:175584-175593</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.1109/ACCESS.2019.2956652</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doaj.org/article/81a0746f145d4475b4252e62fb9a2272</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://ieeexplore.ieee.org/document/8917590/</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="u">https://doaj.org/toc/2169-3536</subfield><subfield code="y">Journal toc</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_DOAJ</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-PHA</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_11</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_20</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_23</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_24</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_31</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_39</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_60</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_62</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_63</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_65</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_69</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_73</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_95</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_105</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_151</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_161</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_170</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_213</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_230</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_285</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_293</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_370</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_602</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2014</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4012</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4037</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4125</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4126</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4249</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4305</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4306</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4307</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4313</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4322</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4323</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4324</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4325</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4335</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4338</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4367</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4700</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">7</subfield><subfield code="j">2019</subfield><subfield code="h">175584-175593</subfield></datafield></record></collection>
score	7.401087

Nicht das Richtige dabei?

Schreiben Sie uns!

Modeling and Recognition of Driving Fatigue State Based on R-R Intervals of ECG Data

Nicht das Richtige dabei?

Zugang & Verfügbarkeit

Vorhandene Bände

Nicht das Richtige dabei?