{"id":"https://openalex.org/W7155644769","doi":"https://doi.org/10.1007/s44163-026-01320-1","title":"Deep learning-based dynamic prediction of physical energy expenditure and training intensity optimization system for university sports training","display_name":"Deep learning-based dynamic prediction of physical energy expenditure and training intensity optimization system for university sports training","publication_year":2026,"publication_date":"2026-04-25","ids":{"openalex":"https://openalex.org/W7155644769","doi":"https://doi.org/10.1007/s44163-026-01320-1"},"language":"en","primary_location":{"id":"doi:10.1007/s44163-026-01320-1","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s44163-026-01320-1","pdf_url":null,"source":{"id":"https://openalex.org/S4210220416","display_name":"Discover Artificial Intelligence","issn_l":"2731-0809","issn":["2731-0809"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319965","host_organization_name":"Springer Nature","host_organization_lineage":["https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Nature"],"type":"journal"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Discover Artificial Intelligence","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1007/s44163-026-01320-1","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5069665576","display_name":"Zhichen Song","orcid":"https://orcid.org/0000-0001-8316-864X"},"institutions":[{"id":"https://openalex.org/I4210135980","display_name":"Huainan Normal University","ror":"https://ror.org/03n7a5z57","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210135980"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Zhichen Song","raw_affiliation_strings":["Huainan Vocational and Technical College, Huainan, 232001, Anhui, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Huainan Vocational and Technical College, Huainan, 232001, Anhui, China","institution_ids":["https://openalex.org/I4210135980"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5069665576"],"corresponding_institution_ids":["https://openalex.org/I4210135980"],"apc_list":{"value":990,"currency":"EUR","value_usd":1067},"apc_paid":{"value":990,"currency":"EUR","value_usd":1067},"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.48968829,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"6","issue":"1","first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10157","display_name":"Sports Performance and Training","score":0.273499995470047,"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"}},"topics":[{"id":"https://openalex.org/T10157","display_name":"Sports Performance and Training","score":0.273499995470047,"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/T11209","display_name":"Cardiovascular and exercise physiology","score":0.19820000231266022,"subfield":{"id":"https://openalex.org/subfields/2707","display_name":"Complementary and alternative 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/T14413","display_name":"Advanced Technologies in Various Fields","score":0.05249999836087227,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/artificial-neural-network","display_name":"Artificial neural network","score":0.46939998865127563},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4577000141143799},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.42989999055862427},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.4034000039100647},{"id":"https://openalex.org/keywords/mean-squared-error","display_name":"Mean squared error","score":0.4018999934196472},{"id":"https://openalex.org/keywords/energy","display_name":"Energy (signal processing)","score":0.35409998893737793},{"id":"https://openalex.org/keywords/efficient-energy-use","display_name":"Efficient energy use","score":0.35269999504089355},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.33570000529289246}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7583000063896179},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5985999703407288},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.46939998865127563},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4668000042438507},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4577000141143799},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.42989999055862427},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4034000039100647},{"id":"https://openalex.org/C139945424","wikidata":"https://www.wikidata.org/wiki/Q1940696","display_name":"Mean squared error","level":2,"score":0.4018999934196472},{"id":"https://openalex.org/C186370098","wikidata":"https://www.wikidata.org/wiki/Q442787","display_name":"Energy (signal processing)","level":2,"score":0.35409998893737793},{"id":"https://openalex.org/C2742236","wikidata":"https://www.wikidata.org/wiki/Q924713","display_name":"Efficient energy use","level":2,"score":0.35269999504089355},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.33570000529289246},{"id":"https://openalex.org/C150217764","wikidata":"https://www.wikidata.org/wiki/Q6803607","display_name":"Mean absolute percentage error","level":3,"score":0.3305000066757202},{"id":"https://openalex.org/C2777211547","wikidata":"https://www.wikidata.org/wiki/Q17141490","display_name":"Training (meteorology)","level":2,"score":0.31790000200271606},{"id":"https://openalex.org/C519991488","wikidata":"https://www.wikidata.org/wiki/Q28865","display_name":"Python (programming language)","level":2,"score":0.3066999912261963},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.30149999260902405},{"id":"https://openalex.org/C45804977","wikidata":"https://www.wikidata.org/wiki/Q7239673","display_name":"Predictive modelling","level":2,"score":0.2912999987602234},{"id":"https://openalex.org/C155032097","wikidata":"https://www.wikidata.org/wiki/Q798503","display_name":"Backpropagation","level":3,"score":0.28859999775886536},{"id":"https://openalex.org/C2779903281","wikidata":"https://www.wikidata.org/wiki/Q6888026","display_name":"Modalities","level":2,"score":0.2881999909877777},{"id":"https://openalex.org/C2780165032","wikidata":"https://www.wikidata.org/wiki/Q16869822","display_name":"Energy consumption","level":2,"score":0.2847999930381775},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.27219998836517334},{"id":"https://openalex.org/C40969351","wikidata":"https://www.wikidata.org/wiki/Q3516228","display_name":"Word error rate","level":2,"score":0.26489999890327454},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.2506999969482422}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1007/s44163-026-01320-1","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s44163-026-01320-1","pdf_url":null,"source":{"id":"https://openalex.org/S4210220416","display_name":"Discover Artificial Intelligence","issn_l":"2731-0809","issn":["2731-0809"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319965","host_organization_name":"Springer Nature","host_organization_lineage":["https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Nature"],"type":"journal"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Discover Artificial Intelligence","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:385cd37eba2544ba8d891528ccc2d940","is_oa":true,"landing_page_url":"https://doaj.org/article/385cd37eba2544ba8d891528ccc2d940","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":"Discover Artificial Intelligence, Vol 6, Iss 1 (2026)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1007/s44163-026-01320-1","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s44163-026-01320-1","pdf_url":null,"source":{"id":"https://openalex.org/S4210220416","display_name":"Discover Artificial Intelligence","issn_l":"2731-0809","issn":["2731-0809"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319965","host_organization_name":"Springer Nature","host_organization_lineage":["https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Nature"],"type":"journal"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Discover Artificial Intelligence","raw_type":"journal-article"},"sustainable_development_goals":[{"score":0.9195355176925659,"id":"https://metadata.un.org/sdg/7","display_name":"Affordable and clean energy"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":27,"referenced_works":["https://openalex.org/W3014629293","https://openalex.org/W3037521081","https://openalex.org/W3187496905","https://openalex.org/W4308802971","https://openalex.org/W4319993451","https://openalex.org/W4361278200","https://openalex.org/W4377011226","https://openalex.org/W4382198327","https://openalex.org/W4385783554","https://openalex.org/W4388263141","https://openalex.org/W4388945725","https://openalex.org/W4389306409","https://openalex.org/W4389439727","https://openalex.org/W4391388463","https://openalex.org/W4396831230","https://openalex.org/W4399327335","https://openalex.org/W4401466928","https://openalex.org/W4402231802","https://openalex.org/W4403875442","https://openalex.org/W4403969689","https://openalex.org/W4404764590","https://openalex.org/W4405954512","https://openalex.org/W4410791860","https://openalex.org/W4410975369","https://openalex.org/W4411697863","https://openalex.org/W4412944097","https://openalex.org/W4416668156"],"related_works":[],"abstract_inverted_index":{"Accurate":[0],"estimation":[1],"of":[2,209],"physical":[3],"energy":[4,40],"expenditure":[5],"(EE)":[6],"is":[7],"essential":[8],"for":[9,27,60,130,154,163,173,181,240],"optimizing":[10],"training":[11,56,232,243],"intensity":[12,57],"and":[13,31,39,55,80,85,97,107,116,121,177,184,203,216,230,244],"preventing":[14],"overexertion":[15],"in":[16],"university":[17,61,70,241],"sports":[18,242],"programs.":[19],"Existing":[20],"EE":[21,226],"prediction":[22,54],"models":[23],"fail":[24],"to":[25],"account":[26],"individual":[28],"physiological":[29,37,87],"modifications":[30],"the":[32,127,139],"dynamic":[33,53,73,99,122],"relationship":[34],"between":[35,214],"multimodal":[36],"signals":[38,74],"consumption.":[41],"To":[42],"overcome":[43],"these":[44],"limitations,":[45],"this":[46],"research":[47,64],"develops":[48],"a":[49,66,204],"deep":[50],"learning":[51],"(DL)-based":[52],"optimization":[58],"system":[59],"athletes.":[62],"The":[63,111,133,187,219],"uses":[65],"multi-modal":[67],"dataset":[68],"(2500":[69],"athletes)":[71],"containing":[72],"(tri-axial":[75],"acceleration,":[76],"heart":[77],"rate":[78],"(HR),":[79],"electro":[81],"cardio":[82],"gram":[83],"(ECG))":[84],"static":[86,120],"parameters":[88],"Body":[89],"Mass":[90],"Index":[91],"(BMI),":[92],"body-fat":[93],"percentage,":[94],"resting":[95,98],"HR,":[96],"oxygen":[100],"uptake":[101],"(VO2)).":[102],"Data":[103],"pre-processing":[104],"removes":[105],"noise":[106],"normalizes":[108],"signal":[109],"scales.":[110],"feature":[112,157],"extraction":[113],"captures":[114],"temporal":[115,156],"spatial":[117],"features":[118],"from":[119],"signals.":[123],"Feature":[124],"fusion":[125],"integrates":[126],"two":[128],"modalities":[129],"personalized":[131,221],"representation.":[132],"fused":[134],"data":[135],"are":[136],"fed":[137],"into":[138],"proposed":[140,220],"Teaching-Learning":[141],"Fused":[142],"Intelligent":[143],"Convo":[144],"Memory":[145,170],"Network":[146],"(TL-ICMN),":[147],"which":[148],"combines":[149],"Convolutional":[150],"Neural":[151],"Networks":[152],"(CNN)":[153],"local":[155],"learning,":[158],"an":[159,166,236],"Attention":[160],"mechanism":[161],"(AM)":[162],"attribute":[164],"selection,":[165],"Improved":[167],"Long":[168],"Short-Term":[169],"(ILSTM)":[171],"network":[172],"long-term":[174],"dependency":[175],"modelling,":[176],"Teaching-Learning-Based":[178],"Optimization":[179],"(TLBO)":[180],"hyper-parameter":[182],"tuning":[183],"performance":[185,195,245],"enhancement.":[186],"model":[188],"using":[189],"Python":[190],"3.11":[191],"achieves":[192],"strong":[193],"predictive":[194],"with":[196,227],"lower":[197],"error":[198],"rates,":[199],"Training":[200],"time":[201],"(0.5s)":[202],"high":[205,228],"correlation":[206],"coefficient":[207],"(R2)":[208],"0.955,":[210],"representative":[211],"excellent":[212],"agreement":[213],"predicted":[215],"actual":[217],"EE.":[218],"TL-ICMN":[222],"framework":[223],"dynamically":[224],"predicts":[225],"precision":[229],"optimizes":[231],"intensity,":[233],"by":[234],"offering":[235],"intelligent":[237],"decision-support":[238],"tool":[239],"management.":[246]},"counts_by_year":[],"updated_date":"2026-06-10T06:17:36.459440","created_date":"2026-04-26T00:00:00"}
