{"id":"https://openalex.org/W6967193367","doi":"https://doi.org/10.48550/arxiv.2508.04827","title":"A deep learning approach to track eye movements based on events","display_name":"A deep learning approach to track eye movements based on events","publication_year":2025,"publication_date":"2025-08-06","ids":{"openalex":"https://openalex.org/W6967193367","doi":"https://doi.org/10.48550/arxiv.2508.04827"},"language":"en","primary_location":{"id":"doi:10.48550/arxiv.2508.04827","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2508.04827","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2508.04827","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Seth, Chirag","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Seth, Chirag","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Naiken, Divya","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Naiken, Divya","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":null,"display_name":"Lin, Keyan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lin, Keyan","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":true,"primary_topic":{"id":"https://openalex.org/T11707","display_name":"Gaze Tracking and Assistive Technology","score":0.9664999842643738,"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":0.9664999842643738,"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/T11605","display_name":"Visual Attention and Saliency Detection","score":0.015300000086426735,"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/T13219","display_name":"Mind wandering and attention","score":0.004999999888241291,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/interpretability","display_name":"Interpretability","score":0.878000020980835},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.6908000111579895},{"id":"https://openalex.org/keywords/eye-movement","display_name":"Eye movement","score":0.637499988079071},{"id":"https://openalex.org/keywords/eye-tracking","display_name":"Eye tracking","score":0.6151000261306763},{"id":"https://openalex.org/keywords/relevance","display_name":"Relevance (law)","score":0.600600004196167},{"id":"https://openalex.org/keywords/position","display_name":"Position (finance)","score":0.49000000953674316},{"id":"https://openalex.org/keywords/event","display_name":"Event (particle physics)","score":0.454800009727478},{"id":"https://openalex.org/keywords/track","display_name":"Track (disk drive)","score":0.43560001254081726}],"concepts":[{"id":"https://openalex.org/C2781067378","wikidata":"https://www.wikidata.org/wiki/Q17027399","display_name":"Interpretability","level":2,"score":0.878000020980835},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7541000247001648},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7210999727249146},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.6908000111579895},{"id":"https://openalex.org/C153050134","wikidata":"https://www.wikidata.org/wiki/Q760256","display_name":"Eye movement","level":2,"score":0.637499988079071},{"id":"https://openalex.org/C56461940","wikidata":"https://www.wikidata.org/wiki/Q970687","display_name":"Eye tracking","level":2,"score":0.6151000261306763},{"id":"https://openalex.org/C158154518","wikidata":"https://www.wikidata.org/wiki/Q7310970","display_name":"Relevance (law)","level":2,"score":0.600600004196167},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5472999811172485},{"id":"https://openalex.org/C198082294","wikidata":"https://www.wikidata.org/wiki/Q3399648","display_name":"Position (finance)","level":2,"score":0.49000000953674316},{"id":"https://openalex.org/C2779662365","wikidata":"https://www.wikidata.org/wiki/Q5416694","display_name":"Event (particle physics)","level":2,"score":0.454800009727478},{"id":"https://openalex.org/C89992363","wikidata":"https://www.wikidata.org/wiki/Q5961558","display_name":"Track (disk drive)","level":2,"score":0.43560001254081726},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3959999978542328},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.35600000619888306},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.3522000014781952},{"id":"https://openalex.org/C2775936607","wikidata":"https://www.wikidata.org/wiki/Q466845","display_name":"Tracking (education)","level":2,"score":0.35109999775886536},{"id":"https://openalex.org/C90673727","wikidata":"https://www.wikidata.org/wiki/Q901718","display_name":"Product (mathematics)","level":2,"score":0.3327000141143799},{"id":"https://openalex.org/C13662910","wikidata":"https://www.wikidata.org/wiki/Q193139","display_name":"Trajectory","level":2,"score":0.28060001134872437},{"id":"https://openalex.org/C12725497","wikidata":"https://www.wikidata.org/wiki/Q810247","display_name":"Baseline (sea)","level":2,"score":0.27000001072883606},{"id":"https://openalex.org/C18762648","wikidata":"https://www.wikidata.org/wiki/Q42213","display_name":"Work (physics)","level":2,"score":0.2624000012874603},{"id":"https://openalex.org/C107457646","wikidata":"https://www.wikidata.org/wiki/Q207434","display_name":"Human\u2013computer interaction","level":1,"score":0.26030001044273376},{"id":"https://openalex.org/C175154964","wikidata":"https://www.wikidata.org/wiki/Q380077","display_name":"Task analysis","level":3,"score":0.25029999017715454}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2508.04827","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2508.04827","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2508.04827","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2508.04827","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"},"sustainable_development_goals":[{"display_name":"Industry, innovation and infrastructure","id":"https://metadata.un.org/sdg/9","score":0.44673359394073486}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"This":[0],"research":[1],"project":[2],"addresses":[3],"the":[4,19,46,112,136],"challenge":[5],"of":[6,22,29],"accurately":[7],"tracking":[8,33],"eye":[9,32,47],"movements":[10,21],"during":[11],"specific":[12],"events":[13],"by":[14],"leveraging":[15],"previous":[16],"research.":[17],"Given":[18],"rapid":[20],"human":[23,92],"eyes,":[24],"which":[25],"can":[26],"reach":[27],"speeds":[28],"300\u00b0/s,":[30],"precise":[31],"typically":[34],"requires":[35],"expensive":[36],"and":[37,70,83,98,139],"high-speed":[38],"cameras.":[39],"Our":[40],"primary":[41],"objective":[42],"is":[43,78],"to":[44,79,90,133],"locate":[45],"center":[48],"position":[49],"(x,":[50],"y)":[51],"using":[52,86],"inputs":[53],"from":[54],"an":[55,81],"event":[56],"camera.":[57],"Eye":[58],"movement":[59],"analysis":[60],"has":[61],"extensive":[62],"applications":[63],"in":[64,68],"consumer":[65],"electronics,":[66],"especially":[67],"VR":[69],"AR":[71],"product":[72],"development.":[73],"Therefore,":[74],"our":[75],"ultimate":[76],"goal":[77],"develop":[80],"interpretable":[82],"cost-effective":[84],"algorithm":[85],"deep":[87],"learning":[88],"methods":[89],"predict":[91],"attention,":[93],"thereby":[94],"improving":[95],"device":[96],"comfort":[97],"enhancing":[99],"overall":[100],"user":[101],"experience.":[102],"To":[103],"achieve":[104],"this":[105],"goal,":[106],"we":[107,123],"explored":[108],"various":[109],"approaches,":[110],"with":[111],"CNN\\_LSTM":[113],"model":[114],"proving":[115],"most":[116],"effective,":[117],"achieving":[118],"approximately":[119],"81\\%":[120],"accuracy.":[121],"Additionally,":[122],"propose":[124],"future":[125],"work":[126],"focusing":[127],"on":[128],"Layer-wise":[129],"Relevance":[130],"Propagation":[131],"(LRP)":[132],"further":[134],"enhance":[135],"model's":[137],"interpretability":[138],"predictive":[140],"performance.":[141]},"counts_by_year":[],"updated_date":"2025-11-06T06:51:31.235846","created_date":"2025-10-10T00:00:00"}
