{"id":"https://openalex.org/W3037932154","doi":"https://doi.org/10.1109/eais48028.2020.9122768","title":"Unsupervised Driver Workload Learning through Domain Adaptation from Temporal Signals","display_name":"Unsupervised Driver Workload Learning through Domain Adaptation from Temporal Signals","publication_year":2020,"publication_date":"2020-05-01","ids":{"openalex":"https://openalex.org/W3037932154","doi":"https://doi.org/10.1109/eais48028.2020.9122768","mag":"3037932154"},"language":"en","primary_location":{"id":"doi:10.1109/eais48028.2020.9122768","is_oa":false,"landing_page_url":"https://doi.org/10.1109/eais48028.2020.9122768","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE Conference on Evolving and Adaptive Intelligent Systems (EAIS)","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5037546959","display_name":"Yongquan Xie","orcid":"https://orcid.org/0000-0002-7458-5617"},"institutions":[{"id":"https://openalex.org/I4210130704","display_name":"University of Michigan\u2013Dearborn","ror":"https://ror.org/035wtm547","country_code":"US","type":"education","lineage":["https://openalex.org/I4210130704"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yongquan Xie","raw_affiliation_strings":["Department of Electrical and Computer Engineering, University of Michigan-Dearborn, Dearborn, Michigan, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, University of Michigan-Dearborn, Dearborn, Michigan, USA","institution_ids":["https://openalex.org/I4210130704"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5054035359","display_name":"Yi Lu Murphey","orcid":"https://orcid.org/0000-0002-0501-8002"},"institutions":[{"id":"https://openalex.org/I4210130704","display_name":"University of Michigan\u2013Dearborn","ror":"https://ror.org/035wtm547","country_code":"US","type":"education","lineage":["https://openalex.org/I4210130704"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yi Lu Murphey","raw_affiliation_strings":["Department of Electrical and Computer Engineering, University of Michigan-Dearborn, Dearborn, Michigan, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, University of Michigan-Dearborn, Dearborn, Michigan, USA","institution_ids":["https://openalex.org/I4210130704"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I4210130704"],"apc_list":null,"apc_paid":null,"fwci":0.4764,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.63913824,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":95},"biblio":{"volume":"17","issue":null,"first_page":"1","last_page":"8"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11373","display_name":"Sleep and Work-Related Fatigue","score":0.9886999726295471,"subfield":{"id":"https://openalex.org/subfields/3205","display_name":"Experimental and Cognitive Psychology"},"field":{"id":"https://openalex.org/fields/32","display_name":"Psychology"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},"topics":[{"id":"https://openalex.org/T11373","display_name":"Sleep and Work-Related Fatigue","score":0.9886999726295471,"subfield":{"id":"https://openalex.org/subfields/3205","display_name":"Experimental and Cognitive Psychology"},"field":{"id":"https://openalex.org/fields/32","display_name":"Psychology"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T11196","display_name":"Non-Invasive Vital Sign Monitoring","score":0.9858999848365784,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.9786999821662903,"subfield":{"id":"https://openalex.org/subfields/2215","display_name":"Building and Construction"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8231257796287537},{"id":"https://openalex.org/keywords/discriminative-model","display_name":"Discriminative model","score":0.6532461643218994},{"id":"https://openalex.org/keywords/discriminator","display_name":"Discriminator","score":0.6166136860847473},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5950596928596497},{"id":"https://openalex.org/keywords/workload","display_name":"Workload","score":0.5922000408172607},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5794211030006409},{"id":"https://openalex.org/keywords/domain","display_name":"Domain (mathematical analysis)","score":0.4744095504283905},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.43564102053642273},{"id":"https://openalex.org/keywords/labeled-data","display_name":"Labeled data","score":0.4283786416053772},{"id":"https://openalex.org/keywords/data-modeling","display_name":"Data modeling","score":0.4219895601272583},{"id":"https://openalex.org/keywords/entropy","display_name":"Entropy (arrow of time)","score":0.42119792103767395},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3846164345741272},{"id":"https://openalex.org/keywords/database","display_name":"Database","score":0.1348535120487213}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8231257796287537},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.6532461643218994},{"id":"https://openalex.org/C2779803651","wikidata":"https://www.wikidata.org/wiki/Q5282088","display_name":"Discriminator","level":3,"score":0.6166136860847473},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5950596928596497},{"id":"https://openalex.org/C2778476105","wikidata":"https://www.wikidata.org/wiki/Q628539","display_name":"Workload","level":2,"score":0.5922000408172607},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5794211030006409},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.4744095504283905},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.43564102053642273},{"id":"https://openalex.org/C2776145971","wikidata":"https://www.wikidata.org/wiki/Q30673951","display_name":"Labeled data","level":2,"score":0.4283786416053772},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.4219895601272583},{"id":"https://openalex.org/C106301342","wikidata":"https://www.wikidata.org/wiki/Q4117933","display_name":"Entropy (arrow of time)","level":2,"score":0.42119792103767395},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3846164345741272},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.1348535120487213},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0},{"id":"https://openalex.org/C94915269","wikidata":"https://www.wikidata.org/wiki/Q1834857","display_name":"Detector","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/eais48028.2020.9122768","is_oa":false,"landing_page_url":"https://doi.org/10.1109/eais48028.2020.9122768","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE Conference on Evolving and Adaptive Intelligent Systems (EAIS)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10","score":0.699999988079071}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":50,"referenced_works":["https://openalex.org/W1580490094","https://openalex.org/W1731081199","https://openalex.org/W1882958252","https://openalex.org/W1978600950","https://openalex.org/W1993837778","https://openalex.org/W2064447488","https://openalex.org/W2072030458","https://openalex.org/W2099471712","https://openalex.org/W2128053425","https://openalex.org/W2145494108","https://openalex.org/W2158815628","https://openalex.org/W2159291411","https://openalex.org/W2279034837","https://openalex.org/W2293363371","https://openalex.org/W2523098180","https://openalex.org/W2569898400","https://openalex.org/W2573966230","https://openalex.org/W2584009249","https://openalex.org/W2593768305","https://openalex.org/W2762260497","https://openalex.org/W2771289139","https://openalex.org/W2788768841","https://openalex.org/W2795155917","https://openalex.org/W2799012717","https://openalex.org/W2799056151","https://openalex.org/W2810658541","https://openalex.org/W2882781154","https://openalex.org/W2889945482","https://openalex.org/W2926598625","https://openalex.org/W2948065500","https://openalex.org/W2963826681","https://openalex.org/W2964278684","https://openalex.org/W2973152224","https://openalex.org/W2982259084","https://openalex.org/W2996649805","https://openalex.org/W4320013936","https://openalex.org/W6637618735","https://openalex.org/W6639480849","https://openalex.org/W6681588610","https://openalex.org/W6683401423","https://openalex.org/W6683633756","https://openalex.org/W6695692224","https://openalex.org/W6713955831","https://openalex.org/W6746464140","https://openalex.org/W6750109254","https://openalex.org/W6753363134","https://openalex.org/W6754057283","https://openalex.org/W6762963088","https://openalex.org/W6767560061","https://openalex.org/W6772527494"],"related_works":["https://openalex.org/W4293202849","https://openalex.org/W1980965563","https://openalex.org/W1489300767","https://openalex.org/W2387995142","https://openalex.org/W4380714744","https://openalex.org/W4319453655","https://openalex.org/W2089959425","https://openalex.org/W1757117718","https://openalex.org/W2889166412","https://openalex.org/W4389518428"],"abstract_inverted_index":{"Driver":[0],"workload":[1,19,229],"monitoring":[2,15],"is":[3,20,64,70,86,106],"an":[4,22,89,122,203,211],"important":[5],"component":[6],"of":[7,218,228],"the":[8,60,73,97,160,178,199,216,219,223,251],"intelligent":[9],"driver":[10,47],"assistant":[11],"systems":[12],"today.":[13],"Accurately":[14],"a":[16,41,45,107,135,226],"driver's":[17],"real-time":[18],"not":[21,66],"easy":[23],"task.":[24],"It":[25],"requires":[26],"good":[27],"quality":[28],"and":[29,111,147,210],"well-annotated":[30,142],"data":[31,56,94,102,143,152,158,231],"collected":[32,95,232],"from":[33,57,96,103,141,167,233],"drivers":[34,105],"to":[35,52,87,149,186,205,214],"train":[36],"machine":[37],"learning":[38,171],"models.":[39],"Usually,":[40],"model":[42,91,136],"trained":[43],"for":[44,127],"target":[46,54,98,104,154,206],"cannot":[48],"be":[49],"applied":[50],"interchangeably":[51],"another":[53],"unless":[55],"them":[58],"have":[59,163],"similar":[61],"distributions,":[62,177],"which":[63],"however":[65],"commonly":[67],"seen.":[68],"This":[69],"known":[71],"as":[72],"personal":[74],"discrepancy":[75],"problem":[76],"between":[77],"individuals.":[78],"To":[79,115,197],"deal":[80],"with":[81,117,194,240],"this":[82,118,133],"problem,":[83],"one":[84],"method":[85,224],"tune":[88],"existing":[90,169],"using":[92,225],"annotated":[93,101,151],"driver.However,":[99],"obtaining":[100],"time":[108],"consuming,":[109],"labor-costly":[110],"sometimes":[112],"impractical":[113],"procedure.":[114],"cope":[116],"difficulty,":[119],"we":[120,201],"developed":[121],"Adversarial":[123],"Discriminative":[124],"Neural":[125],"Network":[126],"Multi-Temporal":[128],"Signals":[129],"(MTS-ADNN)":[130],"architecture.":[131],"With":[132],"method,":[134],"can":[137,181],"learn":[138],"transferable":[139],"features":[140],"in":[144,153,159],"source":[145],"domain":[146,207,244],"adapts":[148],"non-":[150],"domain,":[155],"even":[156],"if":[157],"two":[161],"domains":[162],"shifted":[164],"distributions.":[165,196],"Different":[166],"many":[168],"adversarial":[170],"architectures":[172],"that":[173,250],"aligns":[174,183],"only":[175],"between-domain":[176,195],"proposed":[179,252],"MTS-ADNN":[180,253],"also":[182],"in-domain":[184,188],"classes":[185],"ensure":[187],"class-conditional":[189],"distributions":[190],"are":[191],"aligned":[192],"jointly":[193],"enhance":[198],"performance,":[200],"added":[202],"entropy-regularizer":[204],"sample":[208],"predictions,":[209],"entropy-aware":[212],"weight":[213],"aggregate":[215],"loss":[217],"discriminator.":[220],"We":[221,236],"evaluated":[222],"set":[227],"estimation":[230],"real-world":[234],"diving.":[235],"compared":[237],"its":[238,255],"performance":[239],"three":[241],"state-of-the-art":[242],"unsupervised":[243],"adaptation":[245],"methods.":[246],"The":[247],"results":[248],"show":[249],"outperforms":[254],"counterparts.":[256]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2021,"cited_by_count":1}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}
