{"id":"https://openalex.org/W3103330131","doi":"https://doi.org/10.1109/access.2020.3023648","title":"Exercise Fatigue Detection Algorithm Based on Video Image Information Extraction","display_name":"Exercise Fatigue Detection Algorithm Based on Video Image Information Extraction","publication_year":2020,"publication_date":"2020-01-01","ids":{"openalex":"https://openalex.org/W3103330131","doi":"https://doi.org/10.1109/access.2020.3023648","mag":"3103330131"},"language":"en","primary_location":{"id":"doi:10.1109/access.2020.3023648","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2020.3023648","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/8948470/09254099.pdf","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://ieeexplore.ieee.org/ielx7/6287639/8948470/09254099.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5048325130","display_name":"Fan Zhang","orcid":"https://orcid.org/0000-0002-1536-286X"},"institutions":[{"id":"https://openalex.org/I4210162190","display_name":"China University of Petroleum, East China","ror":"https://ror.org/05gbn2817","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210162190"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Fan Zhang","raw_affiliation_strings":["Department of Physical Education, China University of Petroleum (East China), Qingdao, China"],"affiliations":[{"raw_affiliation_string":"Department of Physical Education, China University of Petroleum (East China), Qingdao, China","institution_ids":["https://openalex.org/I4210162190"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5078027241","display_name":"Feng Wang","orcid":"https://orcid.org/0000-0002-5443-2533"},"institutions":[{"id":"https://openalex.org/I4210162190","display_name":"China University of Petroleum, East China","ror":"https://ror.org/05gbn2817","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210162190"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Feng Wang","raw_affiliation_strings":["Department of Physical Education, China University of Petroleum (East China), Qingdao, China"],"affiliations":[{"raw_affiliation_string":"Department of Physical Education, China University of Petroleum (East China), Qingdao, China","institution_ids":["https://openalex.org/I4210162190"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5048325130"],"corresponding_institution_ids":["https://openalex.org/I4210162190"],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":6.3952,"has_fulltext":true,"cited_by_count":28,"citation_normalized_percentile":{"value":0.96789021,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":99},"biblio":{"volume":"8","issue":null,"first_page":"199696","last_page":"199709"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T13647","display_name":"AI and Big Data Applications","score":0.9559999704360962,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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/T13647","display_name":"AI and Big Data Applications","score":0.9559999704360962,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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/T11196","display_name":"Non-Invasive Vital Sign Monitoring","score":0.954200029373169,"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/T12994","display_name":"Infrared Thermography in Medicine","score":0.9258000254631042,"subfield":{"id":"https://openalex.org/subfields/2741","display_name":"Radiology, Nuclear Medicine and Imaging"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/softmax-function","display_name":"Softmax function","score":0.744219958782196},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.714493453502655},{"id":"https://openalex.org/keywords/feature-selection","display_name":"Feature selection","score":0.6304534077644348},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.6101483702659607},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6076026558876038},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.573137640953064},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.4740152955055237},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.42615553736686707},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.42584842443466187},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.22233322262763977}],"concepts":[{"id":"https://openalex.org/C188441871","wikidata":"https://www.wikidata.org/wiki/Q7554146","display_name":"Softmax function","level":3,"score":0.744219958782196},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.714493453502655},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.6304534077644348},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.6101483702659607},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6076026558876038},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.573137640953064},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4740152955055237},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.42615553736686707},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.42584842443466187},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.22233322262763977},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/access.2020.3023648","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2020.3023648","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/8948470/09254099.pdf","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:336d7fe1d5fb4fb390be0576a47fc5c0","is_oa":true,"landing_page_url":"https://doaj.org/article/336d7fe1d5fb4fb390be0576a47fc5c0","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-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Access, Vol 8, Pp 199696-199709 (2020)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2020.3023648","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2020.3023648","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/8948470/09254099.pdf","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":[{"score":0.7200000286102295,"display_name":"Decent work and economic growth","id":"https://metadata.un.org/sdg/8"}],"awards":[{"id":"https://openalex.org/G657831596","display_name":null,"funder_award_id":"19CX04022B","funder_id":"https://openalex.org/F4320335787","funder_display_name":"Fundamental Research Funds for the Central Universities"},{"id":"https://openalex.org/G8951484681","display_name":null,"funder_award_id":"Grant","funder_id":"https://openalex.org/F4320335787","funder_display_name":"Fundamental Research Funds for the Central Universities"}],"funders":[{"id":"https://openalex.org/F4320335787","display_name":"Fundamental Research Funds for the Central Universities","ror":null}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3103330131.pdf","grobid_xml":"https://content.openalex.org/works/W3103330131.grobid-xml"},"referenced_works_count":34,"referenced_works":["https://openalex.org/W1066329845","https://openalex.org/W1209748798","https://openalex.org/W1837480624","https://openalex.org/W1992082721","https://openalex.org/W1997998434","https://openalex.org/W2004871065","https://openalex.org/W2022253241","https://openalex.org/W2028570517","https://openalex.org/W2030084517","https://openalex.org/W2062867640","https://openalex.org/W2097840012","https://openalex.org/W2098329693","https://openalex.org/W2114193021","https://openalex.org/W2147376995","https://openalex.org/W2164689416","https://openalex.org/W2207276209","https://openalex.org/W2319319650","https://openalex.org/W2498230662","https://openalex.org/W2550476060","https://openalex.org/W2569302892","https://openalex.org/W2581936448","https://openalex.org/W2582344767","https://openalex.org/W2605126039","https://openalex.org/W2611349832","https://openalex.org/W2736389237","https://openalex.org/W2761881812","https://openalex.org/W2800663094","https://openalex.org/W2887859967","https://openalex.org/W2888688605","https://openalex.org/W2890584425","https://openalex.org/W2903515384","https://openalex.org/W2905975571","https://openalex.org/W2914520563","https://openalex.org/W2918876655"],"related_works":["https://openalex.org/W3107204728","https://openalex.org/W4287591324","https://openalex.org/W3108503355","https://openalex.org/W3090555870","https://openalex.org/W4226420367","https://openalex.org/W3095506574","https://openalex.org/W3190449293","https://openalex.org/W2997424368","https://openalex.org/W4386564352","https://openalex.org/W2952668426"],"abstract_inverted_index":{"Excessive":[0],"psychological":[1],"pressure,":[2],"long":[3],"working":[4],"hours,":[5],"and":[6,14,18,31,46,50,68,112,149,165,173,182,188,202,291,337],"excessive":[7,29],"labor":[8],"intensity":[9],"can":[10,27,255,307,323],"make":[11,217],"people":[12],"exhausted":[13],"affect":[15],"people's":[16],"cognition":[17],"motor":[19],"function.":[20],"Detecting":[21],"the":[22,37,44,48,52,60,66,72,99,103,109,114,121,124,139,142,150,161,166,175,203,207,221,224,229,233,250,259,268,275,283,286,293,309,313,316,325,329,333,339,342],"fatigue":[23,30,88,125,146,155,183,208,264,287,303,326],"state":[24,327],"of":[25,71,102,141,145,154,168,178,206,220,223,271,285,315,328,341],"athletes":[26],"prevent":[28],"sports":[32,186],"injuries.":[33],"This":[34,305],"article":[35,281],"chooses":[36],"adaptive":[38,53,193],"median":[39],"filter":[40],"method":[41,57,306],"to":[42,58,65,85,137,216,241,312],"smooth":[43],"image":[45],"remove":[47],"noise,":[49],"uses":[51],"threshold":[54],"light":[55,62],"equalization":[56],"adjust":[59],"image's":[61],"equalization.":[63],"According":[64],"admission":[67],"rejection":[69],"criteria":[70],"Sequential":[73],"Forward":[74],"Floating":[75],"Selection":[76],"(SFFS)":[77],"algorithm,":[78],"different":[79],"feature":[80,147,152,209,225,243],"parameter":[81,127],"combinations":[82],"are":[83,247],"used":[84,136],"build":[86],"a":[87,263],"motion":[89,156,288],"detection":[90,106,194],"model":[91,107,195],"based":[92,198,331],"on":[93,160,185,199,267,332],"Support":[94],"Vector":[95],"Machine":[96],"(SVM).":[97],"Taking":[98],"classification":[100],"performance":[101],"built":[104,197],"SVM":[105],"as":[108,120,302],"evaluation":[110],"criterion,":[111,279],"using":[113],"sequence":[115],"floating":[116],"forward":[117],"selection":[118,129],"algorithm":[119,130,134],"search":[122],"strategy,":[123],"characteristic":[126],"optimization":[128],"is":[131,135,157,196,212,261],"established.":[132],"The":[133],"reduce":[138],"dimensionality":[140],"full":[143,218],"set":[144],"parameters,":[148,201],"optimal":[151],"subset":[153],"extracted.":[158],"Based":[159,266],"paired":[162],"sample":[163,289],"t-test":[164],"analysis":[167],"variance":[169],"method,":[170],"it":[171,322],"analyzes":[172],"quantifies":[174],"comprehensive":[176,334],"influence":[177],"individual":[179],"athlete":[180,260],"differences":[181],"exercise":[184],"behavior":[187],"eye":[189],"movement":[190],"characteristics.":[191],"An":[192],"personality":[200],"design":[204],"idea":[205],"extraction":[210],"network":[211,235],"analyzed.":[213],"In":[214],"order":[215],"use":[219],"information":[222],"vector":[226],"output":[227,248],"by":[228,249],"fully":[230,238],"connected":[231,239],"layer,":[232],"new":[234],"designs":[236],"two":[237],"layers":[240],"extract":[242],"vectors.":[244],"Two":[245],"types":[246],"Softmax":[251],"loss":[252],"function,":[253],"which":[254],"directly":[256],"determine":[257],"whether":[258],"in":[262],"state.":[265],"PERCLOS":[269,310],"(Percentage":[270],"Eyelid":[272],"Closure":[273],"Over":[274],"Pupil":[276],"over":[277],"time)":[278],"this":[280],"completes":[282],"construction":[284],"set,":[290],"classifies":[292],"face":[294,330],"images":[295],"with":[296],"more":[297],"than":[298],"80%":[299],"eyes":[300],"closed":[301],"samples.":[304],"apply":[308],"criterion":[311],"training":[314],"convolutional":[317],"neural":[318],"network,":[319],"so":[320],"that":[321],"recognize":[324],"facial":[335],"features":[336],"improve":[338],"robustness":[340],"algorithm.":[343]},"counts_by_year":[{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":6},{"year":2022,"cited_by_count":12},{"year":2021,"cited_by_count":5}],"updated_date":"2026-03-27T05:58:40.876381","created_date":"2025-10-10T00:00:00"}
