{"id":"https://openalex.org/W2885686910","doi":"https://doi.org/10.1080/08839514.2018.1505214","title":"Recurrent Neural Network to Forecast Sprint Performance","display_name":"Recurrent Neural Network to Forecast Sprint Performance","publication_year":2018,"publication_date":"2018-08-01","ids":{"openalex":"https://openalex.org/W2885686910","doi":"https://doi.org/10.1080/08839514.2018.1505214","mag":"2885686910"},"language":"en","primary_location":{"id":"doi:10.1080/08839514.2018.1505214","is_oa":false,"landing_page_url":"https://doi.org/10.1080/08839514.2018.1505214","pdf_url":null,"source":{"id":"https://openalex.org/S125501549","display_name":"Applied Artificial Intelligence","issn_l":"0883-9514","issn":["0883-9514","1087-6545"],"is_oa":false,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310320547","host_organization_name":"Taylor & Francis","host_organization_lineage":["https://openalex.org/P4310320547"],"host_organization_lineage_names":["Taylor & Francis"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Applied Artificial Intelligence","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"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/A5090577837","display_name":"Kyle Peterson","orcid":"https://orcid.org/0000-0003-0128-5282"},"institutions":[{"id":"https://openalex.org/I126307644","display_name":"University of Iowa","ror":"https://ror.org/036jqmy94","country_code":"US","type":"education","lineage":["https://openalex.org/I126307644"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Kyle D. Peterson","raw_affiliation_strings":["Sports Science Department, University of Iowa, Iowa City, IA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Sports Science Department, University of Iowa, Iowa City, IA, USA","institution_ids":["https://openalex.org/I126307644"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5090577837"],"corresponding_institution_ids":["https://openalex.org/I126307644"],"apc_list":{"value":2195,"currency":"USD","value_usd":2195},"apc_paid":null,"fwci":0.8714,"has_fulltext":false,"cited_by_count":9,"citation_normalized_percentile":{"value":0.76792595,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":97},"biblio":{"volume":"32","issue":"7-8","first_page":"692","last_page":"706"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10745","display_name":"Heart Rate Variability and Autonomic Control","score":0.9890000224113464,"subfield":{"id":"https://openalex.org/subfields/2705","display_name":"Cardiology and Cardiovascular Medicine"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},"topics":[{"id":"https://openalex.org/T10745","display_name":"Heart Rate Variability and Autonomic Control","score":0.9890000224113464,"subfield":{"id":"https://openalex.org/subfields/2705","display_name":"Cardiology and Cardiovascular Medicine"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T10157","display_name":"Sports Performance and Training","score":0.982200026512146,"subfield":{"id":"https://openalex.org/subfields/2732","display_name":"Orthopedics and Sports Medicine"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9448000192642212,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7092272043228149},{"id":"https://openalex.org/keywords/sprint","display_name":"Sprint","score":0.6019414067268372},{"id":"https://openalex.org/keywords/recurrent-neural-network","display_name":"Recurrent neural network","score":0.5644211173057556},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.480601042509079},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.42090874910354614},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4108924865722656}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7092272043228149},{"id":"https://openalex.org/C2776868573","wikidata":"https://www.wikidata.org/wiki/Q46855","display_name":"Sprint","level":2,"score":0.6019414067268372},{"id":"https://openalex.org/C147168706","wikidata":"https://www.wikidata.org/wiki/Q1457734","display_name":"Recurrent neural network","level":3,"score":0.5644211173057556},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.480601042509079},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.42090874910354614},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4108924865722656},{"id":"https://openalex.org/C115903868","wikidata":"https://www.wikidata.org/wiki/Q80993","display_name":"Software engineering","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1080/08839514.2018.1505214","is_oa":false,"landing_page_url":"https://doi.org/10.1080/08839514.2018.1505214","pdf_url":null,"source":{"id":"https://openalex.org/S125501549","display_name":"Applied Artificial Intelligence","issn_l":"0883-9514","issn":["0883-9514","1087-6545"],"is_oa":false,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310320547","host_organization_name":"Taylor & Francis","host_organization_lineage":["https://openalex.org/P4310320547"],"host_organization_lineage_names":["Taylor & Francis"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Applied Artificial Intelligence","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:9a6b3a6e0d2848b0b00bfc74c190b41d","is_oa":false,"landing_page_url":"https://doaj.org/article/9a6b3a6e0d2848b0b00bfc74c190b41d","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":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Applied Artificial Intelligence, Vol 32, Iss 7-8, Pp 692-706 (2018)","raw_type":"article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":29,"referenced_works":["https://openalex.org/W202376015","https://openalex.org/W1007291376","https://openalex.org/W1485981043","https://openalex.org/W1515021623","https://openalex.org/W1569512666","https://openalex.org/W1678356000","https://openalex.org/W2016210396","https://openalex.org/W2020287812","https://openalex.org/W2041782669","https://openalex.org/W2043273643","https://openalex.org/W2063197448","https://openalex.org/W2064675550","https://openalex.org/W2075604548","https://openalex.org/W2092740479","https://openalex.org/W2092753437","https://openalex.org/W2099534828","https://openalex.org/W2106069978","https://openalex.org/W2110485445","https://openalex.org/W2125847307","https://openalex.org/W2132488726","https://openalex.org/W2135046866","https://openalex.org/W2140107888","https://openalex.org/W2159152372","https://openalex.org/W2262158080","https://openalex.org/W2401671637","https://openalex.org/W2415213271","https://openalex.org/W2560318754","https://openalex.org/W2791815907","https://openalex.org/W3104931199"],"related_works":["https://openalex.org/W2961085424","https://openalex.org/W4306674287","https://openalex.org/W3046775127","https://openalex.org/W3107602296","https://openalex.org/W4394896187","https://openalex.org/W3170094116","https://openalex.org/W4386462264","https://openalex.org/W4364306694","https://openalex.org/W4312192474","https://openalex.org/W4283697347"],"abstract_inverted_index":{"The":[0],"present":[1],"paper":[2],"demonstrates":[3],"that":[4],"the":[5,20,44],"performance":[6,149],"of":[7,19,25,34,94,104,114,124,126,133,141],"an":[8,112,131,151],"elite":[9],"track":[10],"and":[11,71,82,96,107,136],"field":[12],"sprinter":[13],"can":[14],"be":[15],"predicted":[16],"by":[17,60],"means":[18],"dynamic,":[21],"nonlinear":[22],"mathematical":[23],"method":[24],"recurrent":[26],"neural":[27],"networks":[28],"(RNNs).":[29],"Dataset":[30],"considers":[31],"three":[32],"years":[33],"National":[35],"Collegiate":[36],"Athletics":[37],"Association":[38],"(NCAA)":[39],"Division":[40,153],"I":[41,154],"competitions":[42],"where":[43],"student-athlete":[45],"recorded":[46],"heart":[47],"rate":[48],"variability":[49],"two":[50,67],"days":[51],"precedent":[52],"to":[53,100],"each":[54],"competition.":[55],"Input":[56],"parameters":[57],"were":[58,73,98,144],"selected":[59],"transfer":[61],"entropy":[62],"via":[63],"permutation":[64],"tests.":[65],"Subsequently,":[66],"RNN":[68],"topologies,":[69],"Elman":[70],"Jordan,":[72],"trained":[74],"with":[75,79,111],"32":[76],"competitions,":[77,81],"validated":[78],"7":[80],"tested":[83],"against":[84],"6":[85],"held-out":[86,118],"competitions.":[87],"Resultant":[88],"RNNs,":[89],"which":[90],"possess":[91],"a":[92],"sense":[93],"time":[95],"memory,":[97],"able":[99],"learn":[101],"time-dependent":[102],"sequence":[103],"acute":[105],"adaptation":[106],"predict":[108],"race":[109],"times":[110],"error":[113],"0.09\u20130.16":[115],"s":[116],"on":[117],"test":[119],"data.":[120],"Root":[121],"mean":[122],"sum":[123],"differences":[125],"successive":[127],"R-R":[128],"intervals":[129],"(RMSSD),":[130],"indicator":[132,140],"parasympathetic":[134],"tone,":[135],"direct":[137],"current":[138],"biopotentials,":[139],"active":[142],"wakefulness,":[143],"most":[145],"predictive":[146],"toward":[147],"competitive":[148],"for":[150],"NCAA":[152],"male":[155],"sprinter.":[156]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2022,"cited_by_count":3},{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":2},{"year":2019,"cited_by_count":1}],"updated_date":"2026-05-06T08:25:59.206177","created_date":"2025-10-10T00:00:00"}