{"id":"https://openalex.org/W4206037082","doi":"https://doi.org/10.3390/s22020545","title":"Gaze Tracking Based on Concatenating Spatial-Temporal Features","display_name":"Gaze Tracking Based on Concatenating Spatial-Temporal Features","publication_year":2022,"publication_date":"2022-01-11","ids":{"openalex":"https://openalex.org/W4206037082","doi":"https://doi.org/10.3390/s22020545","pmid":"https://pubmed.ncbi.nlm.nih.gov/35062502"},"language":"en","primary_location":{"id":"doi:10.3390/s22020545","is_oa":true,"landing_page_url":"https://doi.org/10.3390/s22020545","pdf_url":"https://www.mdpi.com/1424-8220/22/2/545/pdf?version=1641901557","source":{"id":"https://openalex.org/S101949793","display_name":"Sensors","issn_l":"1424-8220","issn":["1424-8220"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Sensors","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj","pubmed"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.mdpi.com/1424-8220/22/2/545/pdf?version=1641901557","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5064660160","display_name":"Bor\u2010Jiunn Hwang","orcid":"https://orcid.org/0000-0002-7302-1019"},"institutions":[{"id":"https://openalex.org/I192703390","display_name":"Ming Chuan University","ror":"https://ror.org/02pgvzy25","country_code":"TW","type":"education","lineage":["https://openalex.org/I192703390"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"Bor-Jiunn Hwang","raw_affiliation_strings":["Department of Computer and Communication Engineering, Ming Chuan University, Taoyuan 333, Taiwan"],"raw_orcid":"https://orcid.org/0000-0002-7302-1019","affiliations":[{"raw_affiliation_string":"Department of Computer and Communication Engineering, Ming Chuan University, Taoyuan 333, Taiwan","institution_ids":["https://openalex.org/I192703390"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101471125","display_name":"Huihui Chen","orcid":"https://orcid.org/0000-0002-2321-4349"},"institutions":[{"id":"https://openalex.org/I192703390","display_name":"Ming Chuan University","ror":"https://ror.org/02pgvzy25","country_code":"TW","type":"education","lineage":["https://openalex.org/I192703390"]}],"countries":["TW"],"is_corresponding":true,"raw_author_name":"Hui-Hui Chen","raw_affiliation_strings":["Department of Computer and Communication Engineering, Ming Chuan University, Taoyuan 333, Taiwan"],"raw_orcid":"https://orcid.org/0000-0002-2321-4349","affiliations":[{"raw_affiliation_string":"Department of Computer and Communication Engineering, Ming Chuan University, Taoyuan 333, Taiwan","institution_ids":["https://openalex.org/I192703390"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5037214156","display_name":"Chaur\u2010Heh Hsieh","orcid":"https://orcid.org/0000-0002-6956-6040"},"institutions":[{"id":"https://openalex.org/I4210108113","display_name":"Yango University","ror":"https://ror.org/01eqh1863","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210108113"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chaur-Heh Hsieh","raw_affiliation_strings":["College of Artificial Intelligence, Yango University, Fuzhou 350015, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Artificial Intelligence, Yango University, Fuzhou 350015, China","institution_ids":["https://openalex.org/I4210108113"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5035509220","display_name":"Huang Deng-yu","orcid":null},"institutions":[{"id":"https://openalex.org/I192703390","display_name":"Ming Chuan University","ror":"https://ror.org/02pgvzy25","country_code":"TW","type":"education","lineage":["https://openalex.org/I192703390"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"Deng-Yu Huang","raw_affiliation_strings":["Department of Computer and Communication Engineering, Ming Chuan University, Taoyuan 333, Taiwan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer and Communication Engineering, Ming Chuan University, Taoyuan 333, Taiwan","institution_ids":["https://openalex.org/I192703390"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5101471125"],"corresponding_institution_ids":["https://openalex.org/I192703390"],"apc_list":{"value":2400,"currency":"CHF","value_usd":2598},"apc_paid":{"value":2400,"currency":"CHF","value_usd":2598},"fwci":0.6791,"has_fulltext":true,"cited_by_count":6,"citation_normalized_percentile":{"value":0.71158283,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":97},"biblio":{"volume":"22","issue":"2","first_page":"545","last_page":"545"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11707","display_name":"Gaze Tracking and Assistive Technology","score":0.9998999834060669,"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":0.9998999834060669,"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":0.9815999865531921,"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/T13731","display_name":"Advanced Computing and Algorithms","score":0.9797000288963318,"subfield":{"id":"https://openalex.org/subfields/3322","display_name":"Urban Studies"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8488562107086182},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7293452620506287},{"id":"https://openalex.org/keywords/gaze","display_name":"Gaze","score":0.7174681425094604},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6697114706039429},{"id":"https://openalex.org/keywords/pooling","display_name":"Pooling","score":0.6539185047149658},{"id":"https://openalex.org/keywords/normalization","display_name":"Normalization (sociology)","score":0.5660302639007568},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5217978954315186},{"id":"https://openalex.org/keywords/transfer-of-learning","display_name":"Transfer of learning","score":0.4510582387447357},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.44650155305862427},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.4147115647792816}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8488562107086182},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7293452620506287},{"id":"https://openalex.org/C2779916870","wikidata":"https://www.wikidata.org/wiki/Q14467155","display_name":"Gaze","level":2,"score":0.7174681425094604},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6697114706039429},{"id":"https://openalex.org/C70437156","wikidata":"https://www.wikidata.org/wiki/Q7228652","display_name":"Pooling","level":2,"score":0.6539185047149658},{"id":"https://openalex.org/C136886441","wikidata":"https://www.wikidata.org/wiki/Q926129","display_name":"Normalization (sociology)","level":2,"score":0.5660302639007568},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5217978954315186},{"id":"https://openalex.org/C150899416","wikidata":"https://www.wikidata.org/wiki/Q1820378","display_name":"Transfer of learning","level":2,"score":0.4510582387447357},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.44650155305862427},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4147115647792816},{"id":"https://openalex.org/C19165224","wikidata":"https://www.wikidata.org/wiki/Q23404","display_name":"Anthropology","level":1,"score":0.0},{"id":"https://openalex.org/C144024400","wikidata":"https://www.wikidata.org/wiki/Q21201","display_name":"Sociology","level":0,"score":0.0}],"mesh":[{"descriptor_ui":"D000084542","descriptor_name":"Eye-Tracking Technology","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D000084542","descriptor_name":"Eye-Tracking Technology","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D000084542","descriptor_name":"Eye-Tracking Technology","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D000084542","descriptor_name":"Eye-Tracking Technology","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D013997","descriptor_name":"Time Factors","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D013997","descriptor_name":"Time Factors","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D013997","descriptor_name":"Time Factors","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D013997","descriptor_name":"Time Factors","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D016571","descriptor_name":"Neural Networks, Computer","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D016571","descriptor_name":"Neural Networks, Computer","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D016571","descriptor_name":"Neural Networks, Computer","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D016571","descriptor_name":"Neural Networks, Computer","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D057567","descriptor_name":"Memory, Long-Term","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D057567","descriptor_name":"Memory, Long-Term","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false}],"locations_count":5,"locations":[{"id":"doi:10.3390/s22020545","is_oa":true,"landing_page_url":"https://doi.org/10.3390/s22020545","pdf_url":"https://www.mdpi.com/1424-8220/22/2/545/pdf?version=1641901557","source":{"id":"https://openalex.org/S101949793","display_name":"Sensors","issn_l":"1424-8220","issn":["1424-8220"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Sensors","raw_type":"journal-article"},{"id":"pmid:35062502","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/35062502","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Sensors (Basel, Switzerland)","raw_type":null},{"id":"pmh:oai:doaj.org/article:95e11ad146e741db9e683eef44c3db85","is_oa":true,"landing_page_url":"https://doaj.org/article/95e11ad146e741db9e683eef44c3db85","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Sensors, Vol 22, Iss 2, p 545 (2022)","raw_type":"article"},{"id":"pmh:oai:mdpi.com:/1424-8220/22/2/545/","is_oa":true,"landing_page_url":"https://dx.doi.org/10.3390/s22020545","pdf_url":null,"source":{"id":"https://openalex.org/S4306400947","display_name":"MDPI (MDPI AG)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I4210097602","host_organization_name":"Multidisciplinary Digital Publishing Institute (Switzerland)","host_organization_lineage":["https://openalex.org/I4210097602"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Sensors; Volume 22; Issue 2; Pages: 545","raw_type":"Text"},{"id":"pmh:oai:pubmedcentral.nih.gov:8781122","is_oa":true,"landing_page_url":"https://www.ncbi.nlm.nih.gov/pmc/articles/8781122","pdf_url":null,"source":{"id":"https://openalex.org/S2764455111","display_name":"PubMed Central","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Sensors (Basel)","raw_type":"Text"}],"best_oa_location":{"id":"doi:10.3390/s22020545","is_oa":true,"landing_page_url":"https://doi.org/10.3390/s22020545","pdf_url":"https://www.mdpi.com/1424-8220/22/2/545/pdf?version=1641901557","source":{"id":"https://openalex.org/S101949793","display_name":"Sensors","issn_l":"1424-8220","issn":["1424-8220"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Sensors","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4206037082.pdf","grobid_xml":"https://content.openalex.org/works/W4206037082.grobid-xml"},"referenced_works_count":31,"referenced_works":["https://openalex.org/W1485009520","https://openalex.org/W1995694455","https://openalex.org/W2007571554","https://openalex.org/W2027879843","https://openalex.org/W2042906110","https://openalex.org/W2044697119","https://openalex.org/W2064675550","https://openalex.org/W2086650884","https://openalex.org/W2100379672","https://openalex.org/W2209610041","https://openalex.org/W2598992495","https://openalex.org/W2743391672","https://openalex.org/W2769581371","https://openalex.org/W2778268008","https://openalex.org/W2798167946","https://openalex.org/W2914496424","https://openalex.org/W2941239341","https://openalex.org/W2979556775","https://openalex.org/W2992948675","https://openalex.org/W3026168715","https://openalex.org/W3036066034","https://openalex.org/W3036534532","https://openalex.org/W3042064084","https://openalex.org/W3042726568","https://openalex.org/W3077420696","https://openalex.org/W3083654920","https://openalex.org/W3131573026","https://openalex.org/W3162685422","https://openalex.org/W3198511864","https://openalex.org/W6719625255","https://openalex.org/W6795680614"],"related_works":["https://openalex.org/W2953234277","https://openalex.org/W2626256601","https://openalex.org/W2810679507","https://openalex.org/W3183901164","https://openalex.org/W3135818718","https://openalex.org/W4290188444","https://openalex.org/W3167935049","https://openalex.org/W3003905048","https://openalex.org/W2253429366","https://openalex.org/W3127975138"],"abstract_inverted_index":{"Based":[0],"on":[1,19,177],"experimental":[2],"observations,":[3],"there":[4],"is":[5,105,180],"a":[6,98,120,203,245],"correlation":[7],"between":[8,39],"time":[9],"and":[10,76,88,112,129,171,199,227,260],"consecutive":[11],"gaze":[12,20,137,211],"positions":[13],"in":[14,57,139],"visual":[15],"behaviors.":[16],"Previous":[17],"studies":[18],"point":[21,212],"estimation":[22,138],"usually":[23],"use":[24],"images":[25,65],"as":[26,66,145],"the":[27,36,45,48,55,67,80,83,109,134,140,146,152,160,165,219,228,241,250,257,263],"input":[28,68,147],"for":[29,197,205,210],"model":[30,154,259,265],"trainings":[31],"without":[32],"taking":[33],"into":[34],"account":[35],"sequence":[37],"relationship":[38],"image":[40],"data.":[41,69,149],"In":[42,101,150],"addition":[43],"to":[44,53,73,96,107,132,191],"spatial":[46,75,110,128],"features,":[47,111],"temporal":[49,77,115,130],"features":[50,78,131],"are":[51,94,216,267],"considered":[52],"improve":[54,133],"accuracy":[56,248],"this":[58,102],"paper":[59,118],"by":[60,158],"using":[61],"videos":[62,144],"instead":[63],"of":[64,136,142,162,167,186,208,221,230,256,262],"To":[70,194],"be":[71,156],"able":[72],"capture":[74],"at":[79],"same":[81],"time,":[82],"convolutional":[84],"neural":[85],"network":[86,93,124],"(CNN)":[87],"long":[89],"short-term":[90],"memory":[91],"(LSTM)":[92],"introduced":[95],"build":[97],"training":[99,148,187,198],"model.":[100],"way,":[103],"CNN":[104,121],"used":[106,224],"extract":[108],"LSTM":[113,123,163],"correlates":[114],"features.":[116],"This":[117],"presents":[119],"Concatenating":[122],"(CCLN)":[125],"that":[126,183,240],"concatenates":[127],"performance":[135],"case":[141],"time-series":[143],"addition,":[151],"proposed":[153,242],"can":[155],"optimized":[157],"exploring":[159],"numbers":[161],"layers,":[164],"influence":[166],"batch":[168],"normalization":[169],"(BN)":[170],"global":[172],"average":[173],"pooling":[174],"layer":[175],"(GAP)":[176],"CCLN.":[178],"It":[179],"generally":[181],"believed":[182],"larger":[184],"amounts":[185],"data":[188,196],"will":[189],"lead":[190],"better":[192,246],"models.":[193],"provide":[195],"prediction,":[200],"we":[201],"propose":[202],"method":[204,243],"constructing":[206],"datasets":[207],"video":[209],"estimation.":[213],"The":[214],"issues":[215],"studied,":[217],"including":[218],"effectiveness":[220],"different":[222],"commonly":[223],"general":[225,264],"models":[226],"impact":[229],"transfer":[231],"learning.":[232],"Through":[233],"exhaustive":[234],"evaluation,":[235],"it":[236],"has":[237],"been":[238],"proved":[239],"achieves":[244],"prediction":[247],"than":[249],"existing":[251],"CNN-based":[252],"methods.":[253],"Finally,":[254],"93.1%":[255],"best":[258],"92.6%":[261],"MobileNet":[266],"obtained.":[268]},"counts_by_year":[{"year":2025,"cited_by_count":3},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":1}],"updated_date":"2026-05-21T06:26:12.895304","created_date":"2025-10-10T00:00:00"}
