{"id":"https://openalex.org/W7125962108","doi":"https://doi.org/10.1109/smc58881.2025.11342625","title":"Ultra-Lightweight Eye State Detection for Resource-Constrained Devices","display_name":"Ultra-Lightweight Eye State Detection for Resource-Constrained Devices","publication_year":2025,"publication_date":"2025-10-05","ids":{"openalex":"https://openalex.org/W7125962108","doi":"https://doi.org/10.1109/smc58881.2025.11342625"},"language":null,"primary_location":{"id":"doi:10.1109/smc58881.2025.11342625","is_oa":false,"landing_page_url":"https://doi.org/10.1109/smc58881.2025.11342625","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE International Conference on Systems, Man, and Cybernetics (SMC)","raw_type":"proceedings-article"},"type":"article","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/A5124075110","display_name":"Mike Otto","orcid":null},"institutions":[{"id":"https://openalex.org/I4210164098","display_name":"Hannover Re (Germany)","ror":"https://ror.org/05qc7pm63","country_code":"DE","type":"company","lineage":["https://openalex.org/I4210164098"]}],"countries":["DE"],"is_corresponding":true,"raw_author_name":"Mike Otto","raw_affiliation_strings":["Hannover University of Applied Sciences,Faculty of Electrical Engineering and Information Technology,Hannover,Germany"],"affiliations":[{"raw_affiliation_string":"Hannover University of Applied Sciences,Faculty of Electrical Engineering and Information Technology,Hannover,Germany","institution_ids":["https://openalex.org/I4210164098"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5112813906","display_name":"Jeff Will","orcid":null},"institutions":[{"id":"https://openalex.org/I4210164098","display_name":"Hannover Re (Germany)","ror":"https://ror.org/05qc7pm63","country_code":"DE","type":"company","lineage":["https://openalex.org/I4210164098"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Jens Christian Will","raw_affiliation_strings":["Hannover University of Applied Sciences,Faculty of Electrical Engineering and Information Technology,Hannover,Germany"],"affiliations":[{"raw_affiliation_string":"Hannover University of Applied Sciences,Faculty of Electrical Engineering and Information Technology,Hannover,Germany","institution_ids":["https://openalex.org/I4210164098"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5057560612","display_name":"Hanno Homann","orcid":null},"institutions":[{"id":"https://openalex.org/I4210164098","display_name":"Hannover Re (Germany)","ror":"https://ror.org/05qc7pm63","country_code":"DE","type":"company","lineage":["https://openalex.org/I4210164098"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Hanno Homann","raw_affiliation_strings":["Hannover University of Applied Sciences,Faculty of Electrical Engineering and Information Technology,Hannover,Germany"],"affiliations":[{"raw_affiliation_string":"Hannover University of Applied Sciences,Faculty of Electrical Engineering and Information Technology,Hannover,Germany","institution_ids":["https://openalex.org/I4210164098"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5124075110"],"corresponding_institution_ids":["https://openalex.org/I4210164098"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.76534008,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"835","last_page":"840"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11707","display_name":"Gaze Tracking and Assistive Technology","score":0.8521999716758728,"subfield":{"id":"https://openalex.org/subfields/1709","display_name":"Human-Computer Interaction"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11707","display_name":"Gaze Tracking and Assistive Technology","score":0.8521999716758728,"subfield":{"id":"https://openalex.org/subfields/1709","display_name":"Human-Computer Interaction"},"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/T10429","display_name":"EEG and Brain-Computer Interfaces","score":0.027400000020861626,"subfield":{"id":"https://openalex.org/subfields/2805","display_name":"Cognitive Neuroscience"},"field":{"id":"https://openalex.org/fields/28","display_name":"Neuroscience"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T11373","display_name":"Sleep and Work-Related Fatigue","score":0.01679999940097332,"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/software-deployment","display_name":"Software deployment","score":0.6240000128746033},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.5967000126838684},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5557000041007996},{"id":"https://openalex.org/keywords/state","display_name":"State (computer science)","score":0.49889999628067017},{"id":"https://openalex.org/keywords/bridge","display_name":"Bridge (graph theory)","score":0.48559999465942383},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.47429999709129333},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.4129999876022339}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6967999935150146},{"id":"https://openalex.org/C105339364","wikidata":"https://www.wikidata.org/wiki/Q2297740","display_name":"Software deployment","level":2,"score":0.6240000128746033},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.5967000126838684},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5791000127792358},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5557000041007996},{"id":"https://openalex.org/C48103436","wikidata":"https://www.wikidata.org/wiki/Q599031","display_name":"State (computer science)","level":2,"score":0.49889999628067017},{"id":"https://openalex.org/C100776233","wikidata":"https://www.wikidata.org/wiki/Q2532492","display_name":"Bridge (graph theory)","level":2,"score":0.48559999465942383},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.47429999709129333},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.4129999876022339},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.38920000195503235},{"id":"https://openalex.org/C2777735758","wikidata":"https://www.wikidata.org/wiki/Q817765","display_name":"Path (computing)","level":2,"score":0.3741999864578247},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.3659000098705292},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.3190999925136566},{"id":"https://openalex.org/C46743427","wikidata":"https://www.wikidata.org/wiki/Q1341685","display_name":"Inference engine","level":3,"score":0.3125999867916107},{"id":"https://openalex.org/C56461940","wikidata":"https://www.wikidata.org/wiki/Q970687","display_name":"Eye tracking","level":2,"score":0.29510000348091125},{"id":"https://openalex.org/C163258240","wikidata":"https://www.wikidata.org/wiki/Q25342","display_name":"Power (physics)","level":2,"score":0.28769999742507935},{"id":"https://openalex.org/C149635348","wikidata":"https://www.wikidata.org/wiki/Q193040","display_name":"Embedded system","level":1,"score":0.27730000019073486},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.2750000059604645},{"id":"https://openalex.org/C98025372","wikidata":"https://www.wikidata.org/wiki/Q477538","display_name":"Systems architecture","level":3,"score":0.2694999873638153},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.26840001344680786},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.25270000100135803}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/smc58881.2025.11342625","is_oa":false,"landing_page_url":"https://doi.org/10.1109/smc58881.2025.11342625","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE International Conference on Systems, Man, and Cybernetics (SMC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":6,"referenced_works":["https://openalex.org/W1832036627","https://openalex.org/W2899973923","https://openalex.org/W2982083293","https://openalex.org/W4221105443","https://openalex.org/W4286487870","https://openalex.org/W4386607611"],"related_works":[],"abstract_inverted_index":{"Eye":[0,94],"state":[1,178],"detection":[2,179],"plays":[3],"a":[4,79,116,171],"vital":[5],"role":[6],"in":[7,104,130,180],"safety-critical":[8],"applications":[9],"such":[10],"as":[11],"driver":[12],"monitoring":[13,144],"systems":[14],"and":[15,39,97,109,168],"medical":[16,153],"diagnostic":[17],"or":[18,52],"therapeutic":[19],"devices.":[20],"This":[21,133],"study":[22],"explores":[23],"ultra-lightweight":[24],"convolutional":[25],"neural":[26],"network":[27],"architectures":[28,103],"tailored":[29],"for":[30,174],"real-time":[31,176],"deployment":[32],"on":[33,49,75,91,115],"resource-constrained":[34],"embedded":[35,131,181],"platforms,":[36],"where":[37,141],"power":[38],"processing":[40],"capabilities":[41],"are":[42],"severely":[43],"limited.Unlike":[44],"traditional":[45],"approaches":[46],"that":[47,158],"rely":[48],"complex":[50],"models":[51,160],"post-processing":[53],"techniques,":[54],"our":[55],"solution":[56],"emphasizes":[57],"low-latency":[58],"inference":[59,107],"while":[60],"maintaining":[61],"high":[62],"classification":[63],"accuracy.":[64],"To":[65],"achieve":[66],"this,":[67],"we":[68],"combine":[69],"efficient":[70],"eye":[71,143,177],"region":[72],"extraction":[73],"based":[74],"facial":[76],"landmarks":[77],"with":[78],"custom-designed":[80],"CNN":[81],"featuring":[82],"just":[83],"over":[84],"260,000":[85],"parameters.":[86],"The":[87],"model":[88,110],"is":[89,113,135,145],"evaluated":[90],"the":[92,121,126,138,164],"MRL":[93],"State":[95],"dataset":[96],"demonstrates":[98],"competitive":[99],"results,":[100],"surpassing":[101],"existing":[102],"terms":[105],"of":[106,137],"speed":[108],"size.Our":[111],"implementation":[112],"validated":[114],"Raspberry":[117],"Pi":[118],"5":[119],"using":[120],"Camera":[122],"Module":[123],"3,":[124],"highlighting":[125],"system\u2019s":[127],"practical":[128],"applicability":[129],"environments.":[132],"research":[134],"part":[136],"DeepLightAI":[139],"project,":[140],"continuous":[142],"required":[146],"to":[147],"ensure":[148],"patient":[149],"safety":[150],"under":[151],"high-intensity":[152],"lighting":[154],"conditions.The":[155],"results":[156],"show":[157],"lightweight":[159],"can":[161],"effectively":[162],"bridge":[163],"gap":[165],"between":[166],"performance":[167],"efficiency,":[169],"offering":[170],"viable":[172],"path":[173],"real-world,":[175],"applications.":[182]},"counts_by_year":[],"updated_date":"2026-02-23T20:09:44.859080","created_date":"2026-01-29T00:00:00"}
