{"id":"https://openalex.org/W4415821295","doi":"https://doi.org/10.1109/access.2025.3628273","title":"Driver Activity Recognition With Vision Transformer Using Time\u2013Frequency Representations Derived From Wrist-Worn Sensors","display_name":"Driver Activity Recognition With Vision Transformer Using Time\u2013Frequency Representations Derived From Wrist-Worn Sensors","publication_year":2025,"publication_date":"2025-01-01","ids":{"openalex":"https://openalex.org/W4415821295","doi":"https://doi.org/10.1109/access.2025.3628273"},"language":"en","primary_location":{"id":"doi:10.1109/access.2025.3628273","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2025.3628273","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1109/access.2025.3628273","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5042174785","display_name":"Yusuke Sakai","orcid":"https://orcid.org/0000-0001-8810-4813"},"institutions":[{"id":"https://openalex.org/I185088104","display_name":"Tokyo City University","ror":"https://ror.org/04dt6bw53","country_code":"JP","type":"education","lineage":["https://openalex.org/I185088104"]}],"countries":["JP"],"is_corresponding":true,"raw_author_name":"Yusuke Sakai","raw_affiliation_strings":["Research Center for Space Science, Advanced Research Laboratories, Tokyo City University, Setagaya-ku, Japan"],"affiliations":[{"raw_affiliation_string":"Research Center for Space Science, Advanced Research Laboratories, Tokyo City University, Setagaya-ku, Japan","institution_ids":["https://openalex.org/I185088104"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5072270755","display_name":"Takuma Akiduki","orcid":"https://orcid.org/0000-0002-2064-0633"},"institutions":[{"id":"https://openalex.org/I66906201","display_name":"University of Yamanashi","ror":"https://ror.org/059x21724","country_code":"JP","type":"education","lineage":["https://openalex.org/I66906201"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Takuma Akiduki","raw_affiliation_strings":["Department of Mechanical Engineering, Faculty of Engineering, University of Yamanashi, Kofu, Yamanashi, Japan","Department of Mechanical Engineering, Faculty of Engineering, University of Yamanashi, 4-3-11, Takeda, Kofu, Yamanashi, Japan"],"affiliations":[{"raw_affiliation_string":"Department of Mechanical Engineering, Faculty of Engineering, University of Yamanashi, Kofu, Yamanashi, Japan","institution_ids":["https://openalex.org/I66906201"]},{"raw_affiliation_string":"Department of Mechanical Engineering, Faculty of Engineering, University of Yamanashi, 4-3-11, Takeda, Kofu, Yamanashi, Japan","institution_ids":["https://openalex.org/I66906201"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5054155656","display_name":"M. Meyer-Conde","orcid":"https://orcid.org/0000-0003-2230-6310"},"institutions":[{"id":"https://openalex.org/I185088104","display_name":"Tokyo City University","ror":"https://ror.org/04dt6bw53","country_code":"JP","type":"education","lineage":["https://openalex.org/I185088104"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Marco Meyer-Conde","raw_affiliation_strings":["Research Center for Space Science, Advanced Research Laboratories, Tokyo City University, Setagaya-ku, Japan"],"affiliations":[{"raw_affiliation_string":"Research Center for Space Science, Advanced Research Laboratories, Tokyo City University, Setagaya-ku, Japan","institution_ids":["https://openalex.org/I185088104"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5049271542","display_name":"Hirotaka Takahashi","orcid":"https://orcid.org/0000-0003-0596-4397"},"institutions":[{"id":"https://openalex.org/I185088104","display_name":"Tokyo City University","ror":"https://ror.org/04dt6bw53","country_code":"JP","type":"education","lineage":["https://openalex.org/I185088104"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Hirotaka Takahashi","raw_affiliation_strings":["Research Center for Space Science, Advanced Research Laboratories, Tokyo City University, Setagaya-ku, Japan"],"affiliations":[{"raw_affiliation_string":"Research Center for Space Science, Advanced Research Laboratories, Tokyo City University, Setagaya-ku, Japan","institution_ids":["https://openalex.org/I185088104"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5042174785"],"corresponding_institution_ids":["https://openalex.org/I185088104"],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.35190143,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"13","issue":null,"first_page":"188839","last_page":"188854"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11373","display_name":"Sleep and Work-Related Fatigue","score":0.46540001034736633,"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":0.46540001034736633,"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/T10444","display_name":"Context-Aware Activity Recognition Systems","score":0.0786999985575676,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10667","display_name":"Emotion and Mood Recognition","score":0.07000000029802322,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/activity-recognition","display_name":"Activity recognition","score":0.6791999936103821},{"id":"https://openalex.org/keywords/transformer","display_name":"Transformer","score":0.5723000168800354},{"id":"https://openalex.org/keywords/wearable-computer","display_name":"Wearable computer","score":0.4828000068664551},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.44110000133514404},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.43720000982284546},{"id":"https://openalex.org/keywords/wavelet-transform","display_name":"Wavelet transform","score":0.42080000042915344},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4097000062465668}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7390000224113464},{"id":"https://openalex.org/C121687571","wikidata":"https://www.wikidata.org/wiki/Q4677630","display_name":"Activity recognition","level":2,"score":0.6791999936103821},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6080999970436096},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.5723000168800354},{"id":"https://openalex.org/C150594956","wikidata":"https://www.wikidata.org/wiki/Q1334829","display_name":"Wearable computer","level":2,"score":0.4828000068664551},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.44110000133514404},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.43720000982284546},{"id":"https://openalex.org/C196216189","wikidata":"https://www.wikidata.org/wiki/Q2867","display_name":"Wavelet transform","level":3,"score":0.42080000042915344},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4097000062465668},{"id":"https://openalex.org/C46286280","wikidata":"https://www.wikidata.org/wiki/Q2414958","display_name":"Discrete wavelet transform","level":4,"score":0.37529999017715454},{"id":"https://openalex.org/C87833898","wikidata":"https://www.wikidata.org/wiki/Q1060280","display_name":"Advanced driver assistance systems","level":2,"score":0.37369999289512634},{"id":"https://openalex.org/C47432892","wikidata":"https://www.wikidata.org/wiki/Q831390","display_name":"Wavelet","level":2,"score":0.367900013923645},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.35510000586509705},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.34549999237060547},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3433000147342682},{"id":"https://openalex.org/C54290928","wikidata":"https://www.wikidata.org/wiki/Q4845080","display_name":"Wearable technology","level":3,"score":0.2754000127315521},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.2752000093460083},{"id":"https://openalex.org/C175154964","wikidata":"https://www.wikidata.org/wiki/Q380077","display_name":"Task analysis","level":3,"score":0.26030001044273376},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.25999999046325684},{"id":"https://openalex.org/C23224414","wikidata":"https://www.wikidata.org/wiki/Q176769","display_name":"Hidden Markov model","level":2,"score":0.2549000084400177},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.2517000138759613}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/access.2025.3628273","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2025.3628273","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:422168c25cdd4700af42af6935017531","is_oa":true,"landing_page_url":"https://doaj.org/article/422168c25cdd4700af42af6935017531","pdf_url":null,"source":{"id":"https://openalex.org/S112646816","display_name":"SHILAP Revista de lepidopterolog\u00eda","issn_l":"0300-5267","issn":["0300-5267","2340-4078"],"is_oa":true,"is_in_doaj":true,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Access, Vol 13, Pp 188839-188854 (2025)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2025.3628273","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2025.3628273","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Driving":[0],"activity":[1,49],"recognition":[2,50,80,90],"is":[3,15],"crucial":[4],"for":[5],"improving":[6],"road":[7],"safety":[8],"and":[9,36,99,116,158],"preventing":[10],"accidents,":[11],"as":[12,33,123],"distracted":[13],"driving":[14,149],"a":[16,108,117,124,167,173,183],"major":[17],"cause":[18],"of":[19,143,169,186],"traffic":[20],"incidents.":[21],"Recently,":[22],"driver":[23,48],"status":[24],"monitoring":[25],"systems":[26],"using":[27,51,107,162,166],"wrist-worn":[28],"sensors":[29],"have":[30,46,55],"been":[31],"developed":[32],"small,":[34],"low-cost,":[35],"user-friendly":[37],"approaches":[38],"to":[39,88,139],"support":[40],"safe":[41],"driving.":[42],"Although":[43],"previous":[44,136],"studies":[45,68],"explored":[47],"wearable":[52],"sensors,":[53],"most":[54],"focused":[56],"on":[57],"analyzing":[58],"time-series":[59],"sensor":[60,170],"data":[61,145],"obtained":[62],"through":[63],"traditional":[64],"statistical":[65],"models.":[66,102,164],"Subsequent":[67],"revealed":[69],"that":[70,78,175],"frequency-domain":[71],"analysis":[72],"can":[73],"provide":[74],"additional":[75],"informative":[76],"features":[77,104],"enhance":[79],"performance.":[81],"Therefore,":[82],"this":[83],"study":[84],"investigated":[85],"the":[86,141,153],"extent":[87],"which":[89],"performance":[91,133,179],"could":[92],"be":[93],"improved":[94],"by":[95],"incorporating":[96],"time\u2013frequency":[97],"information":[98],"deep":[100,125],"learning":[101,126],"Time\u2013frequency":[103],"were":[105],"derived":[106],"short-time":[109],"Fourier":[110],"transform":[111],"(STFT)":[112],"or":[113],"wavelet":[114],"transform,":[115],"vision":[118],"transformer":[119],"(ViT)":[120],"was":[121],"employed":[122],"architecture.":[127],"Consequently,":[128],"our":[129],"approach":[130],"achieved":[131],"superior":[132],"compared":[134],"with":[135,182],"studies.":[137],"Furthermore,":[138],"address":[140],"challenge":[142],"limited":[144,184],"availability":[146],"in":[147,177],"real-world":[148],"scenarios,":[150],"we":[151],"evaluated":[152],"model":[154],"under":[155],"small-sample":[156],"conditions":[157],"demonstrated":[159],"its":[160],"effectiveness":[161],"pretrained":[163],"Experiments":[165],"subset":[168],"channels":[171],"identified":[172],"configuration":[174],"resulted":[176],"minimal":[178],"degradation,":[180],"even":[181],"number":[185],"channels.":[187]},"counts_by_year":[],"updated_date":"2026-03-07T16:01:11.037858","created_date":"2025-11-03T00:00:00"}