ADABase: A Multimodal Dataset for Cognitive Load Estimation

Driver monitoring systems play an important role in lower to mid-level autonomous vehicles. Our work focuses on the detection of cognitive load as a component of driver-state estimation to improve traffic safety. By inducing single and dual-task workloads of increasing intensity on 51 subjects, whil...

Full description

Saved in:
Bibliographic Details
Published inSensors (Basel, Switzerland) Vol. 23; no. 1; p. 340
Main Authors Oppelt, Maximilian P., Foltyn, Andreas, Deuschel, Jessica, Lang, Nadine R., Holzer, Nina, Eskofier, Bjoern M., Yang, Seung Hee
Format Journal Article
LanguageEnglish
Published Switzerland MDPI AG 28.12.2022
MDPI
Subjects
affective computing
Artificial intelligence
Automation
Automobile Driving - psychology
autonomous driving
Business metrics
Cognition
Cognitive load
Datasets
Emotions
Humans
Machine Learning
multimodal dataset
Physiology
Reaction Time
Vehicles
Workload
Workloads
cognitive load
machine learning
affective computing
multimodal dataset
autonomous driving
Online AccessGet full text

Cover

Abstract Driver monitoring systems play an important role in lower to mid-level autonomous vehicles. Our work focuses on the detection of cognitive load as a component of driver-state estimation to improve traffic safety. By inducing single and dual-task workloads of increasing intensity on 51 subjects, while continuously measuring signals from multiple modalities, based on physiological measurements such as ECG, EDA, EMG, PPG, respiration rate, skin temperature and eye tracker data, as well as behavioral measurements such as action units extracted from facial videos, performance metrics like reaction time and subjective feedback using questionnaires, we create ADABase (Autonomous Driving Cognitive Load Assessment Database) As a reference method to induce cognitive load onto subjects, we use the well-established n-back test, in addition to our novel simulator-based k-drive test, motivated by real-world semi-autonomously vehicles. We extract expert features of all measurements and find significant changes in multiple modalities. Ultimately we train and evaluate machine learning algorithms using single and multimodal inputs to distinguish cognitive load levels. We carefully evaluate model behavior and study feature importance. In summary, we introduce a novel cognitive load test, create a cognitive load database, validate changes using statistical tests, introduce novel classification and regression tasks for machine learning and train and evaluate machine learning models.
AbstractList Driver monitoring systems play an important role in lower to mid-level autonomous vehicles. Our work focuses on the detection of cognitive load as a component of driver-state estimation to improve traffic safety. By inducing single and dual-task workloads of increasing intensity on 51 subjects, while continuously measuring signals from multiple modalities, based on physiological measurements such as ECG, EDA, EMG, PPG, respiration rate, skin temperature and eye tracker data, as well as behavioral measurements such as action units extracted from facial videos, performance metrics like reaction time and subjective feedback using questionnaires, we create ADABase (Autonomous Driving Cognitive Load Assessment Database) As a reference method to induce cognitive load onto subjects, we use the well-established n-back test, in addition to our novel simulator-based k-drive test, motivated by real-world semi-autonomously vehicles. We extract expert features of all measurements and find significant changes in multiple modalities. Ultimately we train and evaluate machine learning algorithms using single and multimodal inputs to distinguish cognitive load levels. We carefully evaluate model behavior and study feature importance. In summary, we introduce a novel cognitive load test, create a cognitive load database, validate changes using statistical tests, introduce novel classification and regression tasks for machine learning and train and evaluate machine learning models.Driver monitoring systems play an important role in lower to mid-level autonomous vehicles. Our work focuses on the detection of cognitive load as a component of driver-state estimation to improve traffic safety. By inducing single and dual-task workloads of increasing intensity on 51 subjects, while continuously measuring signals from multiple modalities, based on physiological measurements such as ECG, EDA, EMG, PPG, respiration rate, skin temperature and eye tracker data, as well as behavioral measurements such as action units extracted from facial videos, performance metrics like reaction time and subjective feedback using questionnaires, we create ADABase (Autonomous Driving Cognitive Load Assessment Database) As a reference method to induce cognitive load onto subjects, we use the well-established n-back test, in addition to our novel simulator-based k-drive test, motivated by real-world semi-autonomously vehicles. We extract expert features of all measurements and find significant changes in multiple modalities. Ultimately we train and evaluate machine learning algorithms using single and multimodal inputs to distinguish cognitive load levels. We carefully evaluate model behavior and study feature importance. In summary, we introduce a novel cognitive load test, create a cognitive load database, validate changes using statistical tests, introduce novel classification and regression tasks for machine learning and train and evaluate machine learning models.
Driver monitoring systems play an important role in lower to mid-level autonomous vehicles. Our work focuses on the detection of cognitive load as a component of driver-state estimation to improve traffic safety. By inducing single and dual-task workloads of increasing intensity on 51 subjects, while continuously measuring signals from multiple modalities, based on physiological measurements such as ECG, EDA, EMG, PPG, respiration rate, skin temperature and eye tracker data, as well as behavioral measurements such as action units extracted from facial videos, performance metrics like reaction time and subjective feedback using questionnaires, we create ADABase ( A utonomous D riving Cognitive Load A ssessment Data base ) As a reference method to induce cognitive load onto subjects, we use the well-established n -back test, in addition to our novel simulator-based k -drive test, motivated by real-world semi-autonomously vehicles. We extract expert features of all measurements and find significant changes in multiple modalities. Ultimately we train and evaluate machine learning algorithms using single and multimodal inputs to distinguish cognitive load levels. We carefully evaluate model behavior and study feature importance. In summary, we introduce a novel cognitive load test, create a cognitive load database, validate changes using statistical tests, introduce novel classification and regression tasks for machine learning and train and evaluate machine learning models.
Driver monitoring systems play an important role in lower to mid-level autonomous vehicles. Our work focuses on the detection of cognitive load as a component of driver-state estimation to improve traffic safety. By inducing single and dual-task workloads of increasing intensity on 51 subjects, while continuously measuring signals from multiple modalities, based on measurements such as ECG, EDA, EMG, PPG, respiration rate, skin temperature and eye tracker data, as well as measurements such as action units extracted from facial videos, metrics like reaction time and feedback using questionnaires, we create ( utonomous riving Cognitive Load ssessment Data ) As a reference method to induce cognitive load onto subjects, we use the well-established -back test, in addition to our novel simulator-based -drive test, motivated by real-world semi-autonomously vehicles. We extract expert features of all measurements and find significant changes in multiple modalities. Ultimately we train and evaluate machine learning algorithms using single and multimodal inputs to distinguish cognitive load levels. We carefully evaluate model behavior and study feature importance. In summary, we introduce a novel cognitive load test, create a cognitive load database, validate changes using statistical tests, introduce novel classification and regression tasks for machine learning and train and evaluate machine learning models.
Driver monitoring systems play an important role in lower to mid-level autonomous vehicles. Our work focuses on the detection of cognitive load as a component of driver-state estimation to improve traffic safety. By inducing single and dual-task workloads of increasing intensity on 51 subjects, while continuously measuring signals from multiple modalities, based on physiological measurements such as ECG, EDA, EMG, PPG, respiration rate, skin temperature and eye tracker data, as well as behavioral measurements such as action units extracted from facial videos, performance metrics like reaction time and subjective feedback using questionnaires, we create ADABase (Autonomous Driving Cognitive Load Assessment Database) As a reference method to induce cognitive load onto subjects, we use the well-established n-back test, in addition to our novel simulator-based k-drive test, motivated by real-world semi-autonomously vehicles. We extract expert features of all measurements and find significant changes in multiple modalities. Ultimately we train and evaluate machine learning algorithms using single and multimodal inputs to distinguish cognitive load levels. We carefully evaluate model behavior and study feature importance. In summary, we introduce a novel cognitive load test, create a cognitive load database, validate changes using statistical tests, introduce novel classification and regression tasks for machine learning and train and evaluate machine learning models.
Author Deuschel, Jessica
Holzer, Nina
Eskofier, Bjoern M.
Yang, Seung Hee
Oppelt, Maximilian P.
Foltyn, Andreas
Lang, Nadine R.
AuthorAffiliation 1 Department Digital Health Systems, Fraunhofer IIS, Fraunhofer Institute for Integrated Circuits IIS, 91058 Erlangen, Germany
3 Department Sensory Perception and Analytics, Fraunhofer IIS, Fraunhofer Institute for Integrated Circuits IIS, 91058 Erlangen, Germany
4 Artificial Intelligence in Biomedical Speech Processing Lab, Department Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-University Erlangen Nuremberg, 91052 Erlangen, Germany
2 Machine Learning and Data Analytics Lab (MaD Lab), Department Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-University Erlangen Nuremberg, 91052 Erlangen, Germany
AuthorAffiliation_xml – name: 3 Department Sensory Perception and Analytics, Fraunhofer IIS, Fraunhofer Institute for Integrated Circuits IIS, 91058 Erlangen, Germany
– name: 1 Department Digital Health Systems, Fraunhofer IIS, Fraunhofer Institute for Integrated Circuits IIS, 91058 Erlangen, Germany
– name: 2 Machine Learning and Data Analytics Lab (MaD Lab), Department Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-University Erlangen Nuremberg, 91052 Erlangen, Germany
– name: 4 Artificial Intelligence in Biomedical Speech Processing Lab, Department Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-University Erlangen Nuremberg, 91052 Erlangen, Germany
Author_xml – sequence: 1
  givenname: Maximilian P.
  surname: Oppelt
  fullname: Oppelt, Maximilian P.
– sequence: 2
  givenname: Andreas
  surname: Foltyn
  fullname: Foltyn, Andreas
– sequence: 3
  givenname: Jessica
  surname: Deuschel
  fullname: Deuschel, Jessica
– sequence: 4
  givenname: Nadine R.
  surname: Lang
  fullname: Lang, Nadine R.
– sequence: 5
  givenname: Nina
  surname: Holzer
  fullname: Holzer, Nina
– sequence: 6
  givenname: Bjoern M.
  orcidid: 0000-0002-0417-0336
  surname: Eskofier
  fullname: Eskofier, Bjoern M.
– sequence: 7
  givenname: Seung Hee
  surname: Yang
  fullname: Yang, Seung Hee
BackLink https://www.ncbi.nlm.nih.gov/pubmed/36616939$$D View this record in MEDLINE/PubMed
BookMark eNptkUFvFCEUx4mpse3qwS9gJvFiD2uBxzCDJibbbdUma7zombwBZmUzO1Rgmvjtpbt10zaeII8fv_fgf0qOxjA6Ql4z-h5A0fPEgTIKgj4jJ0xwMW85p0cP9sfkNKUNpRwA2hfkGKRkUoE6IR8Xl4sLTO5Dtai-TUP222BxqC4xl2Ku-hCrZViPPvtbV60C2uoqFQizD-NL8rzHIblX9-uM_Px89WP5db76_uV6uVjNDSiV5zVXWGOPqjO8A4FUtpYLVTd1j8y0rAXJuLGt7Zhh0tTSKOgbZURtOwVdDzNyvffagBt9E0v7-EcH9HpXCHGtMWZvBqe5VRYa7DrRMwG2dJaNkY0TTenMHC-uT3vXzdRtnTVuzBGHR9LHJ6P_pdfhVquWgxK0CN7dC2L4PbmU9dYn44YBRxempHkjWWEFlQV9-wTdhCmO5at2FKfNXR4z8ubhRIdR_mVUgLM9YGJIKbr-gDCq7_LXh_wLe_6ENT7vwiqP8cN_bvwFhFKurw
CitedBy_id crossref_primary_10_3390_s23146597
crossref_primary_10_1080_00140139_2024_2379949
crossref_primary_10_1007_s11571_024_10160_7
crossref_primary_10_3389_fpubh_2023_1195961
crossref_primary_10_1007_s11760_025_03931_0
crossref_primary_10_3389_fcomp_2024_1371181
crossref_primary_10_1051_shsconf_202418901008
crossref_primary_10_1111_ejn_16201
crossref_primary_10_3390_mti9010006
Cites_doi 10.1016/j.dss.2014.02.007
10.1037/e577632012-009
10.1109/ICPR.2008.4761904
10.1016/j.apergo.2018.06.006
10.3389/fpsyg.2014.01344
10.1177/0018720812442086
10.1109/T-AFFC.2011.15
10.1037/0022-3514.54.6.1063
10.1016/j.trf.2005.04.012
10.1145/3173574.3173856
10.3389/fnins.2020.549524
10.3389/feduc.2021.647097
10.1145/3242969.3242985
10.1109/TITS.2005.848368
10.1145/2750858.2807526
10.2307/2136404
10.1109/TAFFC.2019.2927337
10.1109/COGINF.2010.5599796
10.1145/3173574.3174226
10.1109/TITS.2014.2324414
10.3389/fpsyg.2012.00179
10.3389/fphys.2020.00779
10.1016/j.trf.2022.11.013
10.1109/ICDAR.2013.225
10.1080/09658211003702171
10.1016/j.apergo.2010.08.005
10.1177/0018720820929928
10.1109/BIBE.2017.00-12
10.16910/jemr.13.6.1
10.1207/S15326985EP3801_8
10.1037/t27734-000
10.1109/TAFFC.2018.2884461
10.1109/CVPR46437.2021.00753
10.1145/3448111
10.1109/FG47880.2020.00129
10.1080/00140130310001629766
10.1126/science.1736359
10.1037/0022-0663.84.4.429
10.1159/000119003
10.1145/1322192.1322246
10.1145/1864349.1864395
10.1109/eTELEMED.2009.35
10.1109/TAFFC.2015.2392932
10.1121/1.4979340
10.1109/CVPR42600.2020.00525
10.1037/e363942004-001
10.1111/j.1469-8986.2008.00645.x
10.1016/j.psyneuen.2018.07.009
10.1080/001401398186937
10.1109/JBHI.2018.2883751
10.1007/s00779-020-01455-7
10.1007/BF02481078
10.1002/wcs.1222
10.1109/TAFFC.2016.2625250
10.1109/TAFFC.2016.2584042
10.1109/BIA48344.2019.8967457
10.1007/978-3-319-31700-7
10.1007/978-3-319-97909-0_46
10.1109/TAFFC.2017.2731763
10.1145/355017.355028
10.3389/fphys.2018.00727
10.1111/spc3.12057
10.1145/2960413
10.21437/Interspeech.2014-104
10.1145/3530796
10.1111/j.2517-6161.1974.tb00994.x
10.1046/j.1365-2842.1998.00242.x
10.20944/preprints202105.0070.v1
10.1155/2016/8146809
10.1145/2541016.2541083
10.1109/ICASSP.2008.4518041
10.1109/CBMI.2018.8516497
10.1016/S0166-4115(08)62386-9
10.1155/S1110865704406192
10.1371/journal.pone.0043571
10.1177/2331216518800869
10.3390/s20082308
10.1145/2667317.2667320
10.1016/j.aap.2006.09.005
10.1007/978-1-4419-8126-4
10.3389/fphys.2017.00255
10.16910/jemr.14.2.4
10.1080/00140139.2011.604431
10.1177/03611981221090937
10.1037/h0043688
10.1109/THMS.2019.2917194
10.1007/s10648-017-9404-8
10.1145/1979742.1979819
10.1007/978-3-030-70296-0_86
10.1109/TITS.2021.3135266
10.1093/iwc/iwt032
10.1026/0012-1924.51.4.195
10.1016/B978-044451020-4/50031-1
10.1038/sdata.2017.110
10.1177/0018720817690639
10.1109/PerComWorkshops51409.2021.9430936
10.3390/app10113843
10.1007/s42761-023-00191-4
10.1109/ACCESS.2020.2986810
10.1145/1943403.1943454
10.1159/000118611
10.3758/s13428-013-0422-2
10.1016/j.jneumeth.2006.11.017
10.1023/A:1022193728205
10.1016/j.apergo.2017.11.010
10.1145/3329189.3329231
ContentType Journal Article
Copyright 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
2022 by the authors. 2022
Copyright_xml – notice: 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
– notice: 2022 by the authors. 2022
DBID AAYXX
CITATION
CGR
CUY
CVF
ECM
EIF
NPM
3V.
7X7
7XB
88E
8FI
8FJ
8FK
ABUWG
AFKRA
AZQEC
BENPR
CCPQU
DWQXO
FYUFA
GHDGH
K9.
M0S
M1P
PHGZM
PHGZT
PIMPY
PJZUB
PKEHL
PPXIY
PQEST
PQQKQ
PQUKI
PRINS
7X8
5PM
DOA
DOI 10.3390/s23010340
DatabaseName CrossRef
Medline
MEDLINE
MEDLINE (Ovid)
MEDLINE
MEDLINE
PubMed
ProQuest Central (Corporate)
Health & Medical Collection
ProQuest Central (purchase pre-March 2016)
Medical Database (Alumni Edition)
ProQuest Hospital Collection
Hospital Premium Collection (Alumni Edition)
ProQuest Central (Alumni) (purchase pre-March 2016)
ProQuest Central (Alumni)
ProQuest Central UK/Ireland
ProQuest Central Essentials - QC
ProQuest Central
ProQuest One Community College
ProQuest Central
Health Research Premium Collection
Health Research Premium Collection (Alumni)
ProQuest Health & Medical Complete (Alumni)
ProQuest Health & Medical Collection
Medical Database
ProQuest Central Premium
ProQuest One Academic (New)
Publicly Available Content Database
ProQuest Health & Medical Research Collection
ProQuest One Academic Middle East (New)
ProQuest One Health & Nursing
ProQuest One Academic Eastern Edition (DO NOT USE)
ProQuest One Academic
ProQuest One Academic UKI Edition
ProQuest Central China
MEDLINE - Academic
PubMed Central (Full Participant titles)
DOAJ Directory of Open Access Journals
DatabaseTitle CrossRef
MEDLINE
Medline Complete
MEDLINE with Full Text
PubMed
MEDLINE (Ovid)
Publicly Available Content Database
ProQuest One Academic Middle East (New)
ProQuest Central Essentials
ProQuest Health & Medical Complete (Alumni)
ProQuest Central (Alumni Edition)
ProQuest One Community College
ProQuest One Health & Nursing
ProQuest Central China
ProQuest Central
ProQuest Health & Medical Research Collection
Health Research Premium Collection
Health and Medicine Complete (Alumni Edition)
ProQuest Central Korea
Health & Medical Research Collection
ProQuest Central (New)
ProQuest Medical Library (Alumni)
ProQuest One Academic Eastern Edition
ProQuest Hospital Collection
Health Research Premium Collection (Alumni)
ProQuest Hospital Collection (Alumni)
ProQuest Health & Medical Complete
ProQuest Medical Library
ProQuest One Academic UKI Edition
ProQuest One Academic
ProQuest One Academic (New)
ProQuest Central (Alumni)
MEDLINE - Academic
DatabaseTitleList MEDLINE - Academic

MEDLINE
CrossRef

Publicly Available Content Database
Database_xml – sequence: 1
  dbid: DOA
  name: DOAJ Directory of Open Access Journals
  url: https://www.doaj.org/
  sourceTypes: Open Website
– sequence: 2
  dbid: NPM
  name: PubMed
  url: https://proxy.k.utb.cz/login?url=http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed
  sourceTypes: Index Database
– sequence: 3
  dbid: EIF
  name: MEDLINE
  url: https://proxy.k.utb.cz/login?url=https://www.webofscience.com/wos/medline/basic-search
  sourceTypes: Index Database
– sequence: 4
  dbid: BENPR
  name: ProQuest Central
  url: https://www.proquest.com/central
  sourceTypes: Aggregation Database
DeliveryMethod fulltext_linktorsrc
Discipline Engineering
EISSN 1424-8220
ExternalDocumentID oai_doaj_org_article_2d9d37abb4f143d29a67c67e47b341e2
PMC9823940
36616939
10_3390_s23010340
Genre Journal Article
GrantInformation_xml – fundername: Bavarian Ministry of Economic Affairs, Regional Development and Energy through the Center for Analytics Data Applications (ADA-Center) within the framework of "BAYERN DIGITAL II"
  grantid: 20-3410-2-9-8
– fundername: Federal Ministry of Education and Research
  grantid: 16FMD01K, 16FMD02 and 16FMD03
– fundername: Federal Ministry of Education and Research
  grantid: 16FMD01K; 16FMD02; 16FMD03
– fundername: Bavarian Ministry of Economic Affairs, Regional Development and Energy through the Center for Analytics-Data-Applications (ADA-Center) within the framework of “BAYERN DIGITAL II”
  grantid: 20-3410-2-9-8
GroupedDBID ---
123
2WC
53G
5VS
7X7
88E
8FE
8FG
8FI
8FJ
AADQD
AAHBH
AAYXX
ABDBF
ABUWG
ACUHS
ADBBV
ADMLS
AENEX
AFKRA
AFZYC
ALIPV
ALMA_UNASSIGNED_HOLDINGS
BENPR
BPHCQ
BVXVI
CCPQU
CITATION
CS3
D1I
DU5
E3Z
EBD
ESX
F5P
FYUFA
GROUPED_DOAJ
GX1
HH5
HMCUK
HYE
IAO
ITC
KQ8
L6V
M1P
M48
MODMG
M~E
OK1
OVT
P2P
P62
PHGZM
PHGZT
PIMPY
PQQKQ
PROAC
PSQYO
RNS
RPM
TUS
UKHRP
XSB
~8M
CGR
CUY
CVF
ECM
EIF
NPM
PJZUB
PPXIY
3V.
7XB
8FK
AZQEC
DWQXO
K9.
PKEHL
PQEST
PQUKI
PRINS
7X8
5PM
PUEGO
ID FETCH-LOGICAL-c399t-529a5afa9bc2b34a068d249575fa1c8183612cd8db1c16c56c93f79c45db93bf3
IEDL.DBID M48
ISSN 1424-8220
IngestDate Wed Aug 27 01:28:58 EDT 2025
Thu Aug 21 18:38:10 EDT 2025
Fri Jul 11 06:52:13 EDT 2025
Fri Jul 25 20:00:33 EDT 2025
Mon Jul 21 06:00:00 EDT 2025
Tue Jul 01 01:19:40 EDT 2025
Thu Apr 24 22:55:20 EDT 2025
IsDoiOpenAccess true
IsOpenAccess true
IsPeerReviewed true
IsScholarly true
Issue 1
Keywords cognitive load
machine learning
affective computing
multimodal dataset
autonomous driving
Language English
License https://creativecommons.org/licenses/by/4.0
Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
LinkModel DirectLink
MergedId FETCHMERGED-LOGICAL-c399t-529a5afa9bc2b34a068d249575fa1c8183612cd8db1c16c56c93f79c45db93bf3
Notes ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
content type line 23
Main contributing author.
ORCID 0000-0002-0417-0336
OpenAccessLink http://journals.scholarsportal.info/openUrl.xqy?doi=10.3390/s23010340
PMID 36616939
PQID 2761207233
PQPubID 2032333
ParticipantIDs doaj_primary_oai_doaj_org_article_2d9d37abb4f143d29a67c67e47b341e2
pubmedcentral_primary_oai_pubmedcentral_nih_gov_9823940
proquest_miscellaneous_2761982406
proquest_journals_2761207233
pubmed_primary_36616939
crossref_primary_10_3390_s23010340
crossref_citationtrail_10_3390_s23010340
ProviderPackageCode CITATION
AAYXX
PublicationCentury 2000
PublicationDate 20221228
PublicationDateYYYYMMDD 2022-12-28
PublicationDate_xml – month: 12
  year: 2022
  text: 20221228
  day: 28
PublicationDecade 2020
PublicationPlace Switzerland
PublicationPlace_xml – name: Switzerland
– name: Basel
PublicationTitle Sensors (Basel, Switzerland)
PublicationTitleAlternate Sensors (Basel)
PublicationYear 2022
Publisher MDPI AG
MDPI
Publisher_xml – name: MDPI AG
– name: MDPI
References ref_93
Korbach (ref_114) 2018; 30
ref_90
Kirchner (ref_58) 1958; 55
ref_12
Abadi (ref_42) 2021; 12
ref_99
ref_98
Johansson (ref_3) 2005; 8
ref_97
He (ref_118) 2022; 2676
Crundall (ref_31) 1998; 41
ref_96
Sandi (ref_123) 2013; 4
ref_95
Woody (ref_34) 2018; 97
Li (ref_102) 2015; 16
ref_19
ref_16
Zu (ref_18) 2021; 6
Wilson (ref_117) 2021; 5
Smyth (ref_63) 2013; 7
Grassmann (ref_77) 2016; 2016
Abadi (ref_38) 2015; 6
ref_25
ref_24
ref_120
ref_22
ref_21
ref_122
Lisetti (ref_35) 2004; 2004
ref_121
ref_124
Banks (ref_7) 2018; 68
ref_28
ref_27
ref_26
Hussain (ref_45) 2014; 26
Winn (ref_92) 2018; 22
Wang (ref_112) 2014; 62
Smets (ref_82) 2019; 23
McCloy (ref_91) 2017; 141
Rammstedt (ref_57) 2005; 51
ref_72
ref_71
ref_70
Foy (ref_81) 2018; 73
Zhao (ref_104) 2018; 9
Cohen (ref_67) 1983; 24
ref_74
Koelstra (ref_37) 2012; 3
Li (ref_73) 2022; 23
ref_80
McEvoy (ref_2) 2007; 39
Huttunen (ref_23) 2011; 42
ref_89
ref_88
ref_87
ref_85
Healey (ref_51) 2005; 6
Stone (ref_107) 1974; 36
Kirschbaum (ref_62) 1989; 22
Schleifer (ref_83) 2008; 45
ref_50
Hall (ref_105) 1985; 37
Broadbent (ref_113) 2023; 92
He (ref_49) 2019; 49
ref_56
ref_55
ref_54
ref_52
Paxion (ref_5) 2014; 5
Jaeggi (ref_20) 2010; 18
Georgsson (ref_15) 2020; 16
Paas (ref_14) 1992; 84
ref_61
ref_60
Veltman (ref_64) 1993; 28
Markkula (ref_6) 2017; 59
Biondi (ref_76) 2021; 63
Hart (ref_17) 1988; 52
ref_66
ref_65
Paas (ref_11) 2003; 38
Seitz (ref_115) 2022; 58
Peirce (ref_59) 2007; 162
Mehler (ref_53) 2012; 54
ref_119
Leyman (ref_75) 2004; 47
ref_36
ref_32
ref_111
Sweller (ref_10) 1998; 10
Dalmaijer (ref_86) 2014; 46
ref_110
ref_30
Albuquerque (ref_116) 2020; 14
Reimer (ref_79) 2011; 54
ref_39
Watson (ref_68) 1988; 54
Stoeve (ref_94) 2022; 5
ref_103
Subramanian (ref_40) 2018; 9
ref_108
ref_109
ref_47
Baddeley (ref_9) 1992; 255
Gjoreski (ref_106) 2020; 8
ref_44
ref_43
ref_100
ref_41
ref_1
Nourbakhsh (ref_13) 2017; 7
Giannakakis (ref_33) 2019; 13
ref_48
Costa (ref_78) 2017; 8
(ref_84) 1998; 25
Martinez (ref_101) 2019; 10
ref_8
ref_4
Budidha (ref_69) 2020; 11
Taamneh (ref_46) 2017; 4
Yuce (ref_29) 2017; 8
References_xml – volume: 62
  start-page: 1
  year: 2014
  ident: ref_112
  article-title: An Eye-Tracking Study of Website Complexity from Cognitive Load Perspective
  publication-title: Decis. Support Syst.
  doi: 10.1016/j.dss.2014.02.007
– ident: ref_74
– ident: ref_16
  doi: 10.1037/e577632012-009
– ident: ref_36
  doi: 10.1109/ICPR.2008.4761904
– volume: 73
  start-page: 90
  year: 2018
  ident: ref_81
  article-title: Mental workload is reflected in driver behaviour, physiology, eye movements and prefrontal cortex activation
  publication-title: Appl. Ergon.
  doi: 10.1016/j.apergo.2018.06.006
– volume: 5
  start-page: 1344
  year: 2014
  ident: ref_5
  article-title: Mental workload and driving
  publication-title: Front. Psychol.
  doi: 10.3389/fpsyg.2014.01344
– ident: ref_100
– volume: 54
  start-page: 396
  year: 2012
  ident: ref_53
  article-title: Sensitivity of Physiological Measures for Detecting Systematic Variations in Cognitive Demand From a Working Memory Task: An On-Road Study Across Three Age Groups
  publication-title: Hum. Factors: J. Hum. Factors Ergon. Soc.
  doi: 10.1177/0018720812442086
– ident: ref_88
– volume: 3
  start-page: 18
  year: 2012
  ident: ref_37
  article-title: DEAP: A Database for Emotion Analysis; Using Physiological Signals
  publication-title: IEEE Trans. Affect. Comput.
  doi: 10.1109/T-AFFC.2011.15
– volume: 54
  start-page: 1063
  year: 1988
  ident: ref_68
  article-title: Development and Validation of Brief Measures of Positive and Negative Affect: The PANAS Scales
  publication-title: J. Pers. Soc. Psychol.
  doi: 10.1037/0022-3514.54.6.1063
– ident: ref_108
– volume: 8
  start-page: 97
  year: 2005
  ident: ref_3
  article-title: Effects of Visual and Cognitive load in real and simulated motorway driving
  publication-title: Transp. Res. Part F Traffic Psychol. Behav.
  doi: 10.1016/j.trf.2005.04.012
– volume: 58
  start-page: 35
  year: 2022
  ident: ref_115
  article-title: Biosignal-Based Recognition of Cognitive Load: A Systematic Review of Public Datasets and Classifiers
  publication-title: Inf. Syst. Neurosci.
– ident: ref_93
  doi: 10.1145/3173574.3173856
– volume: 14
  start-page: 549524
  year: 2020
  ident: ref_116
  article-title: WAUC: A Multi-Modal Database for Mental Workload Assessment Under Physical Activity
  publication-title: Front. Neurosci.
  doi: 10.3389/fnins.2020.549524
– ident: ref_4
– volume: 6
  start-page: 647097
  year: 2021
  ident: ref_18
  article-title: Subjective Measure of Cognitive Load Depends on Participants’ Content Knowledge Level
  publication-title: Front. Educ.
  doi: 10.3389/feduc.2021.647097
– ident: ref_43
  doi: 10.1145/3242969.3242985
– ident: ref_120
– volume: 6
  start-page: 156
  year: 2005
  ident: ref_51
  article-title: Detecting Stress During Real-World Driving Tasks Using Physiological Sensors
  publication-title: IEEE Trans. Intell. Transp. Syst.
  doi: 10.1109/TITS.2005.848368
– ident: ref_48
– ident: ref_39
  doi: 10.1145/2750858.2807526
– volume: 24
  start-page: 385
  year: 1983
  ident: ref_67
  article-title: A Global Measure of Perceived Stress
  publication-title: J. Health Soc. Behav.
  doi: 10.2307/2136404
– volume: 13
  start-page: 440
  year: 2019
  ident: ref_33
  article-title: Review on psychological stress detection using biosignals
  publication-title: IEEE Trans. Affect. Comput.
  doi: 10.1109/TAFFC.2019.2927337
– ident: ref_124
  doi: 10.1109/COGINF.2010.5599796
– ident: ref_52
  doi: 10.1145/3173574.3174226
– volume: 16
  start-page: 51
  year: 2015
  ident: ref_102
  article-title: Predicting Perceived Visual and Cognitive Distractions of Drivers With Multimodal Features
  publication-title: IEEE Trans. Intell. Transp. Syst.
  doi: 10.1109/TITS.2014.2324414
– ident: ref_122
  doi: 10.3389/fpsyg.2012.00179
– volume: 11
  start-page: 779
  year: 2020
  ident: ref_69
  article-title: Heart Rate Variability (HRV) and Pulse Rate Variability (PRV) for the Assessment of Autonomic Responses
  publication-title: Front. Physiol.
  doi: 10.3389/fphys.2020.00779
– volume: 92
  start-page: 121
  year: 2023
  ident: ref_113
  article-title: Cognitive Load, Working Memory Capacity and Driving Performance: A Preliminary fNIRS and Eye Tracking Study
  publication-title: Transp. Res. Part F Traffic Psychol. Behav.
  doi: 10.1016/j.trf.2022.11.013
– ident: ref_26
  doi: 10.1109/ICDAR.2013.225
– volume: 18
  start-page: 394
  year: 2010
  ident: ref_20
  article-title: The concurrent validity of the N-Back task as a working memory measure
  publication-title: Memory
  doi: 10.1080/09658211003702171
– volume: 42
  start-page: 348
  year: 2011
  ident: ref_23
  article-title: Effect of cognitive load on speech prosody in aviation: Evidence from military simulator flights
  publication-title: Appl. Ergon.
  doi: 10.1016/j.apergo.2010.08.005
– volume: 63
  start-page: 813
  year: 2021
  ident: ref_76
  article-title: Overloaded and at Work: Investigating the Effect of Cognitive Workload on Assembly Task Performance
  publication-title: Hum. Factors J. Hum. Factors Ergon. Soc.
  doi: 10.1177/0018720820929928
– ident: ref_72
  doi: 10.1109/BIBE.2017.00-12
– ident: ref_90
  doi: 10.16910/jemr.13.6.1
– ident: ref_121
– volume: 38
  start-page: 63
  year: 2003
  ident: ref_11
  article-title: Cognitive Load Measurement as a Means to Advance Cognitive Load Theory
  publication-title: Educ. Psychol.
  doi: 10.1207/S15326985EP3801_8
– ident: ref_99
  doi: 10.1037/t27734-000
– volume: 12
  start-page: 479
  year: 2021
  ident: ref_42
  article-title: AMIGOS: A Dataset for Affect, Personality and Mood Research on Individuals and Groups
  publication-title: IEEE Trans. Affect. Comput.
  doi: 10.1109/TAFFC.2018.2884461
– ident: ref_97
  doi: 10.1109/CVPR46437.2021.00753
– volume: 5
  start-page: 1
  year: 2021
  ident: ref_117
  article-title: Objective Measures of Cognitive Load Using Deep Multi-Modal Learning: A Use-Case in Aviation
  publication-title: Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.
  doi: 10.1145/3448111
– ident: ref_103
  doi: 10.1109/FG47880.2020.00129
– volume: 47
  start-page: 625
  year: 2004
  ident: ref_75
  article-title: Cervicobrachial muscle response to cognitive load in a dual-task scenario
  publication-title: Ergonomics
  doi: 10.1080/00140130310001629766
– volume: 255
  start-page: 556
  year: 1992
  ident: ref_9
  article-title: Working Memory
  publication-title: Science
  doi: 10.1126/science.1736359
– volume: 84
  start-page: 429
  year: 1992
  ident: ref_14
  article-title: Training strategies for attaining transfer of problem-solving skill in statistics: A cognitive-load approach
  publication-title: J. Educ. Psychol.
  doi: 10.1037/0022-0663.84.4.429
– volume: 28
  start-page: 72
  year: 1993
  ident: ref_64
  article-title: Indices of mental workload in a complex task environment
  publication-title: Neuropsychobiology
  doi: 10.1159/000119003
– ident: ref_50
– volume: 16
  start-page: 8
  year: 2020
  ident: ref_15
  article-title: NASA RTLX as a Novel Assessment Tool for Determining Cognitive Load and User Acceptance of Expert and User-based Usability Evaluation Methods
  publication-title: Eur. J. Biomed. Inform.
– ident: ref_25
  doi: 10.1145/1322192.1322246
– ident: ref_44
  doi: 10.1145/1864349.1864395
– ident: ref_70
  doi: 10.1109/eTELEMED.2009.35
– volume: 6
  start-page: 209
  year: 2015
  ident: ref_38
  article-title: DECAF: MEG-Based Multimodal Database for Decoding Affective Physiological Responses
  publication-title: IEEE Trans. Affect. Comput.
  doi: 10.1109/TAFFC.2015.2392932
– volume: 141
  start-page: 2440
  year: 2017
  ident: ref_91
  article-title: Pupillometry shows the effort of auditory attention switching
  publication-title: J. Acoust. Soc. Am.
  doi: 10.1121/1.4979340
– ident: ref_96
  doi: 10.1109/CVPR42600.2020.00525
– ident: ref_1
  doi: 10.1037/e363942004-001
– volume: 45
  start-page: 356
  year: 2008
  ident: ref_83
  article-title: Mental stress and trapezius muscle activation under psychomotor challenge: A focus on EMG gaps during computer work
  publication-title: Psychophysiology
  doi: 10.1111/j.1469-8986.2008.00645.x
– volume: 97
  start-page: 149
  year: 2018
  ident: ref_34
  article-title: Social-evaluative threat, cognitive load, and the cortisol and cardiovascular stress response
  publication-title: Psychoneuroendocrinology
  doi: 10.1016/j.psyneuen.2018.07.009
– volume: 41
  start-page: 448
  year: 1998
  ident: ref_31
  article-title: Effects of experience and processing demands on visual information acquisition in drivers
  publication-title: Ergonomics
  doi: 10.1080/001401398186937
– volume: 23
  start-page: 463
  year: 2019
  ident: ref_82
  article-title: Into the Wild: The Challenges of Physiological Stress Detection in Laboratory and Ambulatory Settings
  publication-title: IEEE J. Biomed. Health Inform.
  doi: 10.1109/JBHI.2018.2883751
– ident: ref_109
– ident: ref_119
  doi: 10.1007/s00779-020-01455-7
– volume: 37
  start-page: 27
  year: 1985
  ident: ref_105
  article-title: Limit theorems for the median deviation
  publication-title: Ann. Inst. Stat. Math.
  doi: 10.1007/BF02481078
– volume: 4
  start-page: 245
  year: 2013
  ident: ref_123
  article-title: Stress and cognition
  publication-title: Wires Cogn. Sci.
  doi: 10.1002/wcs.1222
– ident: ref_55
– volume: 9
  start-page: 147
  year: 2018
  ident: ref_40
  article-title: ASCERTAIN: Emotion and Personality Recognition Using Commercial Sensors
  publication-title: IEEE Trans. Affect. Comput.
  doi: 10.1109/TAFFC.2016.2625250
– volume: 8
  start-page: 161
  year: 2017
  ident: ref_29
  article-title: Action Units and Their Cross-Correlations for Prediction of Cognitive Load during Driving
  publication-title: IEEE Trans. Affect. Comput.
  doi: 10.1109/TAFFC.2016.2584042
– ident: ref_47
  doi: 10.1109/BIA48344.2019.8967457
– ident: ref_12
  doi: 10.1007/978-3-319-31700-7
– ident: ref_98
  doi: 10.1007/978-3-319-97909-0_46
– volume: 10
  start-page: 325
  year: 2019
  ident: ref_101
  article-title: Automatic Analysis of Facial Actions: A Survey
  publication-title: IEEE Trans. Affect. Comput.
  doi: 10.1109/TAFFC.2017.2731763
– ident: ref_87
  doi: 10.1145/355017.355028
– volume: 9
  start-page: 727
  year: 2018
  ident: ref_104
  article-title: SQI Quality Evaluation Mechanism of Single-Lead ECG Signal Based on Simple Heuristic Fusion and Fuzzy Comprehensive Evaluation
  publication-title: Front. Physiol.
  doi: 10.3389/fphys.2018.00727
– volume: 7
  start-page: 605
  year: 2013
  ident: ref_63
  article-title: Salivary Cortisol as a Biomarker in Social Science Research: Salivary Cortisol in Social Science Research
  publication-title: Soc. Personal. Psychol. Compass
  doi: 10.1111/spc3.12057
– volume: 7
  start-page: 1
  year: 2017
  ident: ref_13
  article-title: Detecting Users’ Cognitive Load by Galvanic Skin Response with Affective Interference
  publication-title: ACM Trans. Interact. Intell. Syst.
  doi: 10.1145/2960413
– ident: ref_22
  doi: 10.21437/Interspeech.2014-104
– volume: 5
  start-page: 1
  year: 2022
  ident: ref_94
  article-title: Eye Tracking-Based Stress Classification of Athletes in Virtual Reality
  publication-title: Proc. Acm Comput. Graph. Interact. Tech.
  doi: 10.1145/3530796
– volume: 36
  start-page: 111
  year: 1974
  ident: ref_107
  article-title: Cross-Validatory Choice and Assessment of Statistical Predictions
  publication-title: J. R. Stat. Soc. Ser. B (Methodol.)
  doi: 10.1111/j.2517-6161.1974.tb00994.x
– volume: 25
  start-page: 365
  year: 1998
  ident: ref_84
  article-title: Detection of onset and termination of muscle activity in surface electromyograms
  publication-title: J. Oral Rehabil.
  doi: 10.1046/j.1365-2842.1998.00242.x
– ident: ref_8
– ident: ref_71
  doi: 10.20944/preprints202105.0070.v1
– volume: 2016
  start-page: 1
  year: 2016
  ident: ref_77
  article-title: Respiratory Changes in Response to Cognitive Load: A Systematic Review
  publication-title: Neural Plast.
  doi: 10.1155/2016/8146809
– ident: ref_27
  doi: 10.1145/2541016.2541083
– ident: ref_24
  doi: 10.1109/ICASSP.2008.4518041
– ident: ref_28
  doi: 10.1109/CBMI.2018.8516497
– volume: 52
  start-page: 139
  year: 1988
  ident: ref_17
  article-title: Development of NASA-TLX (Task Load Index): Results of Empirical and Theoretical Research
  publication-title: Adv. Psychol.
  doi: 10.1016/S0166-4115(08)62386-9
– volume: 2004
  start-page: 929414
  year: 2004
  ident: ref_35
  article-title: Using Noninvasive Wearable Computers to Recognize Human Emotions from Physiological Signals
  publication-title: EURASIP J. Adv. Signal Process.
  doi: 10.1155/S1110865704406192
– ident: ref_80
  doi: 10.1371/journal.pone.0043571
– volume: 22
  start-page: 233121651880086
  year: 2018
  ident: ref_92
  article-title: Best Practices and Advice for Using Pupillometry to Measure Listening Effort: An Introduction for Those Who Want to Get Started
  publication-title: Trends Hear.
  doi: 10.1177/2331216518800869
– ident: ref_41
  doi: 10.3390/s20082308
– ident: ref_66
– ident: ref_54
  doi: 10.1145/2667317.2667320
– volume: 39
  start-page: 475
  year: 2007
  ident: ref_2
  article-title: The prevalence of, and factors associated with, serious crashes involving a distracting activity
  publication-title: Accid. Anal. Prev.
  doi: 10.1016/j.aap.2006.09.005
– ident: ref_19
  doi: 10.1007/978-1-4419-8126-4
– volume: 8
  start-page: 255
  year: 2017
  ident: ref_78
  article-title: Heart Rate Fragmentation: A New Approach to the Analysis of Cardiac Interbeat Interval Dynamics
  publication-title: Front. Physiol.
  doi: 10.3389/fphys.2017.00255
– ident: ref_111
  doi: 10.16910/jemr.14.2.4
– volume: 54
  start-page: 932
  year: 2011
  ident: ref_79
  article-title: The impact of cognitive workload on physiological arousal in young adult drivers: A field study and simulation validation
  publication-title: Ergonomics
  doi: 10.1080/00140139.2011.604431
– volume: 2676
  start-page: 670
  year: 2022
  ident: ref_118
  article-title: Classification of Driver Cognitive Load: Exploring the Benefits of Fusing Eye-Tracking and Physiological Measures
  publication-title: Transp. Res. Rec. J. Transp. Res. Board
  doi: 10.1177/03611981221090937
– volume: 55
  start-page: 352
  year: 1958
  ident: ref_58
  article-title: Age differences in short-term retention of rapidly changing information
  publication-title: J. Exp. Psychol.
  doi: 10.1037/h0043688
– volume: 49
  start-page: 362
  year: 2019
  ident: ref_49
  article-title: High Cognitive Load Assessment in Drivers Through Wireless Electroencephalography and the Validation of a Modified N -Back Task
  publication-title: IEEE Trans. Hum.-Mach. Syst.
  doi: 10.1109/THMS.2019.2917194
– volume: 30
  start-page: 503
  year: 2018
  ident: ref_114
  article-title: Differentiating Different Types of Cognitive Load: A Comparison of Different Measures
  publication-title: Educ. Psychol. Rev.
  doi: 10.1007/s10648-017-9404-8
– ident: ref_32
  doi: 10.1145/1979742.1979819
– ident: ref_110
  doi: 10.1007/978-3-030-70296-0_86
– volume: 23
  start-page: 14954
  year: 2022
  ident: ref_73
  article-title: Sensitivity of Electrodermal Activity Features for Driver Arousal Measurement in Cognitive Load: The Application in Automated Driving Systems
  publication-title: IEEE Trans. Intell. Transp. Syst.
  doi: 10.1109/TITS.2021.3135266
– ident: ref_21
– volume: 26
  start-page: 256
  year: 2014
  ident: ref_45
  article-title: Automatic Cognitive Load Detection from Face, Physiology, Task Performance and Fusion During Affective Interference
  publication-title: Interact. Comput.
  doi: 10.1093/iwc/iwt032
– volume: 51
  start-page: 195
  year: 2005
  ident: ref_57
  article-title: Kurzversion des Big Five Inventory (BFI-K)
  publication-title: Diagnostica
  doi: 10.1026/0012-1924.51.4.195
– ident: ref_89
  doi: 10.1016/B978-044451020-4/50031-1
– volume: 4
  start-page: 170110
  year: 2017
  ident: ref_46
  article-title: A multimodal dataset for various forms of distracted driving
  publication-title: Sci. Data
  doi: 10.1038/sdata.2017.110
– ident: ref_85
– volume: 59
  start-page: 734
  year: 2017
  ident: ref_6
  article-title: Effects of Cognitive Load on Driving Performance: The Cognitive Control Hypothesis
  publication-title: Hum. Factors J. Hum. Factors Ergon. Soc.
  doi: 10.1177/0018720817690639
– ident: ref_61
  doi: 10.1109/PerComWorkshops51409.2021.9430936
– ident: ref_56
  doi: 10.3390/app10113843
– ident: ref_95
  doi: 10.1007/s42761-023-00191-4
– volume: 8
  start-page: 70590
  year: 2020
  ident: ref_106
  article-title: Machine Learning and End-to-End Deep Learning for Monitoring Driver Distractions From Physiological and Visual Signals
  publication-title: IEEE Access
  doi: 10.1109/ACCESS.2020.2986810
– ident: ref_30
  doi: 10.1145/1943403.1943454
– volume: 22
  start-page: 150
  year: 1989
  ident: ref_62
  article-title: Salivary Cortisol in Psychobiological Research: An Overview
  publication-title: Neuropsychobiology
  doi: 10.1159/000118611
– volume: 46
  start-page: 913
  year: 2014
  ident: ref_86
  article-title: PyGaze: An Open-Source, Cross-Platform Toolbox for Minimal-Effort Programming of Eyetracking Experiments
  publication-title: Behav. Res.
  doi: 10.3758/s13428-013-0422-2
– ident: ref_60
– volume: 162
  start-page: 8
  year: 2007
  ident: ref_59
  article-title: PsychoPy-Psychophysics software in Python
  publication-title: J. Neurosci. Methods
  doi: 10.1016/j.jneumeth.2006.11.017
– volume: 10
  start-page: 251
  year: 1998
  ident: ref_10
  article-title: Cognitive Architecture and Instructional Design
  publication-title: Educ. Psychol. Rev.
  doi: 10.1023/A:1022193728205
– volume: 68
  start-page: 138
  year: 2018
  ident: ref_7
  article-title: Is partially automated driving a bad idea? Observations from an on-road study
  publication-title: Appl. Ergon.
  doi: 10.1016/j.apergo.2017.11.010
– ident: ref_65
  doi: 10.1145/3329189.3329231
SSID ssj0023338
Score 2.444948
Snippet Driver monitoring systems play an important role in lower to mid-level autonomous vehicles. Our work focuses on the detection of cognitive load as a component...
SourceID doaj
pubmedcentral
proquest
pubmed
crossref
SourceType Open Website
Open Access Repository
Aggregation Database
Index Database
Enrichment Source
StartPage 340
SubjectTerms affective computing
Artificial intelligence
Automation
Automobile Driving - psychology
autonomous driving
Business metrics
Cognition
Cognitive load
Datasets
Emotions
Humans
Machine Learning
multimodal dataset
Physiology
Reaction Time
Vehicles
Workload
Workloads
SummonAdditionalLinks – databaseName: DOAJ Directory of Open Access Journals
  dbid: DOA
  link: http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV1LT4QwEJ4YT3owvsVX0HjwQoS2UKondNcYo57cZG-kD4gmCsbF_--UAtk1Jl68toW087Wdb2AeAGeRimXJuAhSEiUBK0kRpEKjqZKakBS4RVzphMen5G7C7qfxdK7Ul_UJc-mBneAuiBGGcqkUK1G1GyJkwnXCC8YVXsBFe_uizuuNqc7Uomh5uTxCFI36ixkS7Sik9gvHnPZpk_T_xix_OkjOaZzbdVjrqKKfuSluwFJRbcLqXALBLbjKRtk16qFLP_PbUNr32uAjI9lgY-MjIfVvev8g_6GWxh_jkXbRitswuR0_39wFXTmEQCOLaNBkFBIFK4XSBNcuwyQ1tnI0j0sZaVS8FNmKNqlRkY4SHSda0JILzWKjBFUl3YHlqq6KPfBThEJS6_VCNNNcpiUzJDQRM0gQlOQenPdiynWXK9yWrHjL0WawEs0HiXpwOgz9cAkyfht0bWU9DLA5rdsGRDrvkM7_QtqDwx6pvDtos5xwXHTIEXAPToZuPCL2v4esivrLjRGppS4e7Dpgh5lQ5CeJoMIDvgD5wlQXe6rXlzYNN77SlpXf_4-1HcAKsXEVkY2VP4Tl5vOrOEK206jjdmN_AyNe-4Y
  priority: 102
  providerName: Directory of Open Access Journals
– databaseName: ProQuest Technology Collection
  dbid: 8FG
  link: http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwfV1LT9wwEB7xuMABAeWRllah6oFLRPxIbMMBLY9lhdqeQOIW-ZHQSpBsd7P_v-Mkm-6iVa-2E9kzHs839jwAvhGT6IILFUlK0ogXNI-ksmiqSBfTHLdIWzrhx8909MQfnpPn7sJt2rlVzs_E5qB2lfV35OcU7W0aC8rY1fhP5KtG-dfVroTGOmwS1DTepUsO73uDi6H91WYTYmjan08RbpOY-XuOBR3UpOpfhS_fu0ku6J3hLux0gDEctBzeg7W83IfthTSCH-BycDu4Rm10EQ7CJqD2rXL4ya2usbEOEZaGN3MvofB7pV14h4LdxiwewNPw7vFmFHVFESKLWKJGw1FpJK9WxlLDuI5T6Xz9aJEUmlhUvwzpZJ10hliS2iS1ihVCWZ44o5gp2CFslFWZH0MokSGaed8XarkVWhbc0dgR7hAmGC0COJuTKbNdxnBfuOI1Q8vBUzTrKRrA137ouE2TsWrQtad1P8Bntm4aqslL1glKRp1yTGhjeIFQzuFqU2FTkXOBqyU5DeBkzqmsE7dp9m9zBHDad6Og-NcPXebVrB2jpAcwARy1jO1nwhClpIqpAMQSy5emutxT_v7VJOPGX_ri8h__P61PsEV93ATxsfAnsFFPZvlnRDO1-dJs2b8SNvLc
  priority: 102
  providerName: ProQuest
Title ADABase: A Multimodal Dataset for Cognitive Load Estimation
URI https://www.ncbi.nlm.nih.gov/pubmed/36616939
https://www.proquest.com/docview/2761207233
https://www.proquest.com/docview/2761982406
https://pubmed.ncbi.nlm.nih.gov/PMC9823940
https://doaj.org/article/2d9d37abb4f143d29a67c67e47b341e2
Volume 23
hasFullText 1
inHoldings 1
isFullTextHit
isPrint
link http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwjV1Lb9QwEB6V9gIHRHkGyiqtOHAJxI_ENghVu-1uK0QrVHWlvUV-JIBUEtimEvx7xnmpQXvoJQc_EnvG1nwTe-YDeENMogsuVCQpSSNe0DySyqKrIl1Mc1wiLXXC2Xl6uuSfV8lqC3qOzU6A1xtdO88ntVxfvfvz--8hbvhP3uNEl_39NcJoEjOOnvsOGiThiQzO-HCYQBlrCK19TBcOicZtgqFx15FZarL3b4Kc_9-cvGWKFo_gYYchw2mr9F3YysvH8OBWZsEn8HF6PJ2hgfoQTsMmxvZn5bDLsa6xsA4RqYZH_cWh8EulXTjHvd6GMT6F5WJ-eXQadTwJkUV4UaMvqTRKXCtjqWFcx6l0nlJaJIUmFi0yQxhjnXSGWJLaJLWKFUJZnjijmCnYM9guqzJ_AaFEHWnmr8NQy63QsuCOxo5wh8jBaBHA215Mme2SiHsui6sMnQkv0WyQaAAHQ9NfbeaMTY1mXtZDA5_suimo1t-ybu9k1CnHhDaGF4juHM42FTYVORc4W5LTAPZ6TWX9AsqowEnHApUfwP5QjXvHH4joMq9u2jZKekwTwPNWscNIGAKXVDEVgBipfDTUcU3543uTnxtf6fnmX97hu6_gPvXxFMTHyO_Bdr2-yV8jyqnNBO6JlcCnXJxMYGc2P_96MWn-GEya1f0PW8X8hQ
linkProvider Scholars Portal
linkToHtml http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV1Lb9QwEB6VcgAOiDcpBQICiUvU-JE4pkJo2-2ypdueWqm34FdaJJqUbirEn-I3dpwXu6ji1qvtOPZ47PnGngfAO6ITVXAho4ySNOIFdVEmDaoqmY2pQxZpUyfsH6TTI_71ODlegT-9L4w3q-zPxOagtpXxd-QbFPVtGgvK2Ofzn5HPGuVfV_sUGi1b7Lnfv1Blm3_aHeP6vqd0snO4PY26rAKRQWFco-YlFY5PSW2oZlzFaWZ9AmaRFIoYlF8Mf2RsZjUxJDVJaiQrhDQ8sVoyXTDs9xbc5gwlufdMn3wZFDyG-l4bvQgr4405wnsSM3-vsiDzmtQA1-HZf80yF-Tc5AHc7wBqOGo56iGsuPIR3FsIW_gYNkfj0RZKv4_hKGwceM8qi5-MVY2FdYgwONzurZLCWaVsuIMHSesj-QSOboRcT2G1rEr3HMIMGUAxb2tDDTdCZQW3NLaEW4QlWokAPvRkyk0XodwnyviRo6biKZoPFA3g7dD0vA3LcV2jLU_roYGPpN0UVBcnebcxc2qlZUJpzQuEjhZnmwqTCscFzpY4GsB6v1J5t73n-V9mDODNUI0b07-2qNJVl20bmXnAFMCzdmGHkTBERalkMgCxtORLQ12uKb-fNsG_sUufzH7t_8N6DXemh_uzfLZ7sPcC7lLvs0G8H_46rNYXl-4lIqlav2rYN4RvN71frgDZ7DAV
linkToPdf http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV1Lb9QwEB6VrYTggHgTWiAgkLhEm9hOHFNVaLe7q5aWVYWo1FvqV2glSNpuKsRf49cxzotdVHHr1XYcZzzj-SaeB8DbSMUyZ1wEKYmSgOXEBqnQaKqkJiQWWaQpnfB5nuwesU_H8fEa_O5iYZxbZXcm1ge1KbX7Rz4kaG-TkBNKh3nrFnE4mX08vwhcBSl309qV02hYZN_--onm22J7b4J7_Y6Q2fTrzm7QVhgINCrmCq0wIXGtUihNFGUyTFLjijHzOJeRRl1G8aXapEZFOkp0nGhBcy40i40SVOUU570F69xZRQNYH0_nh196c4-i9dfkMqJUhMMFgv0opO4vy5IGrAsFXIdu_3XSXNJ6s_twr4Wr_qjhrwewZouHcHcpieEj2BpNRmPUhR_8kV-H8_4oDT4ykRU2Vj6CYn-n81HyD0pp_CkeK03E5GM4uhGCPYFBURb2GfgpsoOkzvOGaKa5THNmSGgiZhCkKMk9eN-RKdNtvnJXNuN7hnaLo2jWU9SDN_3Q8yZJx3WDxo7W_QCXV7tuKC-_Za2YZsQIQ7lUiuUIJA1-bcJ1wi3j-LWRJR5sdjuVtcK-yP6ypgev-24UU3f3IgtbXjVjROrgkwdPm43tV0IRIyWCCg_4ypavLHW1pzg7rVOB45SutP3z_y_rFdxGWckO9ub7G3CHuACOyAXlb8KguryyLxBWVeply78-nNy0yPwB_xw1pw
openUrl ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=ADABase%3A+A+Multimodal+Dataset+for+Cognitive+Load+Estimation&rft.jtitle=Sensors+%28Basel%2C+Switzerland%29&rft.au=Oppelt%2C+Maximilian+P&rft.au=Foltyn%2C+Andreas&rft.au=Deuschel%2C+Jessica&rft.au=Lang%2C+Nadine+R&rft.date=2022-12-28&rft.issn=1424-8220&rft.eissn=1424-8220&rft.volume=23&rft.issue=1&rft_id=info:doi/10.3390%2Fs23010340&rft.externalDBID=NO_FULL_TEXT
thumbnail_l http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1424-8220&client=summon
thumbnail_m http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1424-8220&client=summon
thumbnail_s http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1424-8220&client=summon