{"id":"https://openalex.org/W4408770256","doi":"https://doi.org/10.1007/s42979-025-03739-2","title":"Automated Prediction of Exercise Intensity Using Physiological Data and Deep Learning","display_name":"Automated Prediction of Exercise Intensity Using Physiological Data and Deep Learning","publication_year":2025,"publication_date":"2025-03-24","ids":{"openalex":"https://openalex.org/W4408770256","doi":"https://doi.org/10.1007/s42979-025-03739-2"},"language":"en","primary_location":{"id":"doi:10.1007/s42979-025-03739-2","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s42979-025-03739-2","pdf_url":"https://link.springer.com/content/pdf/10.1007/s42979-025-03739-2.pdf","source":{"id":"https://openalex.org/S4210174798","display_name":"SN Computer Science","issn_l":"2661-8907","issn":["2661-8907","2662-995X"],"is_oa":false,"is_in_doaj":false,"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":"SN Computer Science","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://link.springer.com/content/pdf/10.1007/s42979-025-03739-2.pdf","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5003068838","display_name":"Aref Smiley","orcid":"https://orcid.org/0000-0002-1077-2229"},"institutions":[{"id":"https://openalex.org/I223532165","display_name":"University of Utah","ror":"https://ror.org/03r0ha626","country_code":"US","type":"education","lineage":["https://openalex.org/I223532165"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Aref Smiley","raw_affiliation_strings":["Department of Biomedical Informatics, University of Utah, Salt Lake City, UT, USA"],"raw_orcid":"https://orcid.org/0000-0002-1077-2229","affiliations":[{"raw_affiliation_string":"Department of Biomedical Informatics, University of Utah, Salt Lake City, UT, USA","institution_ids":["https://openalex.org/I223532165"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5086304704","display_name":"Joseph Finkelstein","orcid":"https://orcid.org/0000-0002-8084-7441"},"institutions":[{"id":"https://openalex.org/I223532165","display_name":"University of Utah","ror":"https://ror.org/03r0ha626","country_code":"US","type":"education","lineage":["https://openalex.org/I223532165"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Joseph Finkelstein","raw_affiliation_strings":["Department of Biomedical Informatics, University of Utah, Salt Lake City, UT, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Biomedical Informatics, University of Utah, Salt Lake City, UT, USA","institution_ids":["https://openalex.org/I223532165"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5003068838"],"corresponding_institution_ids":["https://openalex.org/I223532165"],"apc_list":{"value":2290,"currency":"EUR","value_usd":2890},"apc_paid":{"value":2290,"currency":"EUR","value_usd":2890},"fwci":7.2041,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":{"value":0.96419886,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":96,"max":98},"biblio":{"volume":"6","issue":"4","first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11209","display_name":"Cardiovascular and exercise physiology","score":0.9732999801635742,"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"}},"topics":[{"id":"https://openalex.org/T11209","display_name":"Cardiovascular and exercise physiology","score":0.9732999801635742,"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/T10745","display_name":"Heart Rate Variability and Autonomic Control","score":0.9498000144958496,"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/T10444","display_name":"Context-Aware Activity Recognition Systems","score":0.9412999749183655,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5073632597923279},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.48598942160606384},{"id":"https://openalex.org/keywords/intensity","display_name":"Intensity (physics)","score":0.4738001227378845},{"id":"https://openalex.org/keywords/exercise-intensity","display_name":"Exercise intensity","score":0.4458942711353302},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3361663222312927},{"id":"https://openalex.org/keywords/medicine","display_name":"Medicine","score":0.23059678077697754},{"id":"https://openalex.org/keywords/internal-medicine","display_name":"Internal medicine","score":0.14167988300323486},{"id":"https://openalex.org/keywords/heart-rate","display_name":"Heart rate","score":0.06443718075752258},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.05772513151168823},{"id":"https://openalex.org/keywords/blood-pressure","display_name":"Blood pressure","score":0.05229419469833374}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5073632597923279},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.48598942160606384},{"id":"https://openalex.org/C93038891","wikidata":"https://www.wikidata.org/wiki/Q1061524","display_name":"Intensity (physics)","level":2,"score":0.4738001227378845},{"id":"https://openalex.org/C2776811442","wikidata":"https://www.wikidata.org/wiki/Q5420058","display_name":"Exercise intensity","level":4,"score":0.4458942711353302},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3361663222312927},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.23059678077697754},{"id":"https://openalex.org/C126322002","wikidata":"https://www.wikidata.org/wiki/Q11180","display_name":"Internal medicine","level":1,"score":0.14167988300323486},{"id":"https://openalex.org/C2777953023","wikidata":"https://www.wikidata.org/wiki/Q1073121","display_name":"Heart rate","level":3,"score":0.06443718075752258},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.05772513151168823},{"id":"https://openalex.org/C84393581","wikidata":"https://www.wikidata.org/wiki/Q82642","display_name":"Blood pressure","level":2,"score":0.05229419469833374},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1007/s42979-025-03739-2","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s42979-025-03739-2","pdf_url":"https://link.springer.com/content/pdf/10.1007/s42979-025-03739-2.pdf","source":{"id":"https://openalex.org/S4210174798","display_name":"SN Computer Science","issn_l":"2661-8907","issn":["2661-8907","2662-995X"],"is_oa":false,"is_in_doaj":false,"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":"SN Computer Science","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.1007/s42979-025-03739-2","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s42979-025-03739-2","pdf_url":"https://link.springer.com/content/pdf/10.1007/s42979-025-03739-2.pdf","source":{"id":"https://openalex.org/S4210174798","display_name":"SN Computer Science","issn_l":"2661-8907","issn":["2661-8907","2662-995X"],"is_oa":false,"is_in_doaj":false,"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":"SN Computer Science","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":false},"content_urls":{"pdf":"https://content.openalex.org/works/W4408770256.pdf"},"referenced_works_count":31,"referenced_works":["https://openalex.org/W84564939","https://openalex.org/W1702024823","https://openalex.org/W2004008756","https://openalex.org/W2011132634","https://openalex.org/W2046859882","https://openalex.org/W2048973070","https://openalex.org/W2062883667","https://openalex.org/W2076063813","https://openalex.org/W2090048678","https://openalex.org/W2113891167","https://openalex.org/W2128089665","https://openalex.org/W2133589238","https://openalex.org/W2157803415","https://openalex.org/W2220024017","https://openalex.org/W2224094905","https://openalex.org/W2400661814","https://openalex.org/W2564498768","https://openalex.org/W2981080915","https://openalex.org/W4206379319","https://openalex.org/W4297896480","https://openalex.org/W4313259663","https://openalex.org/W4313656140","https://openalex.org/W4320002469","https://openalex.org/W4322739521","https://openalex.org/W4375928116","https://openalex.org/W4377094326","https://openalex.org/W4382632667","https://openalex.org/W4391224059","https://openalex.org/W4395030022","https://openalex.org/W4402282866","https://openalex.org/W4402586437"],"related_works":["https://openalex.org/W2961085424","https://openalex.org/W4306674287","https://openalex.org/W4387369504","https://openalex.org/W4394896187","https://openalex.org/W3170094116","https://openalex.org/W4386462264","https://openalex.org/W3107602296","https://openalex.org/W4364306694","https://openalex.org/W4312192474","https://openalex.org/W4283697347"],"abstract_inverted_index":{"This":[0],"study":[1],"employed":[2],"physiological":[3],"data":[4],"from":[5,157],"wearable":[6],"sensors":[7],"to":[8,126,143,177,193],"build":[9],"deep-learning":[10],"models":[11],"aimed":[12],"at":[13,53,61,67],"predicting":[14],"exercise":[15,128],"exertion":[16,114],"levels.":[17],"A":[18],"total":[19],"of":[20,94,112,211,223],"twenty-one":[21],"healthy":[22],"individuals":[23],"participated":[24],"in":[25,225],"16-minute":[26],"cycling":[27],"sessions,":[28],"during":[29],"which":[30],"real-time":[31],"data,":[32],"including":[33],"ECG,":[34],"pulse":[35],"rate,":[36,97,133],"oxygen":[37,100,136],"saturation,":[38],"and":[39,64,99,104,135,184,195,208],"revolutions":[40],"per":[41,86],"minute":[42],"(RPM),":[43],"were":[44,102,138,154,190],"captured":[45],"across":[46],"three":[47],"different":[48],"intensity":[49,55,69,74],"levels:":[50],"two":[51],"minutes":[52,60,66],"low":[54],"with":[56,70],"no":[57],"resistance,":[58,63],"four":[59],"medium":[62],"ten":[65],"high":[68],"maximum":[71],"resistance.":[72],"Each":[73,118],"level":[75],"was":[76,120,175],"further":[77],"divided":[78,121],"into":[79,122],"consecutive":[80],"2-minute":[81,124],"segments,":[82],"totaling":[83],"eight":[84,123],"segments":[85,125],"session.":[87],"For":[88],"each":[89,141],"segment,":[90],"the":[91,158,165,214,226],"average":[92],"values":[93,131],"RPE,":[95],"heart":[96,132],"RPM,":[98,134],"saturation":[101,137],"calculated":[103],"used":[105,145,192],"as":[106,146,164],"predictive":[107,147],"features.":[108,148],"Participants":[109],"provided":[110],"ratings":[111],"perceived":[113],"(RPE)":[115],"every":[116],"minute.":[117],"session":[119],"analyze":[127],"intensity.":[129],"Average":[130],"computed":[139],"for":[140,181],"segment":[142],"be":[144],"Heart":[149],"rate":[150],"variability":[151],"(HRV)":[152],"parameters":[153],"also":[155],"derived":[156],"ECG":[159],"recordings.":[160],"Averaged":[161],"RPE":[162],"served":[163],"response":[166],"variable.":[167],"The":[168,202],"Minimum":[169],"Redundancy":[170],"Maximum":[171],"Relevance":[172],"(MRMR)":[173],"technique":[174],"applied":[176],"identify":[178],"key":[179],"predictors":[180],"both":[182],"classification":[183,203],"regression":[185,215],"tasks.":[186],"These":[187],"selected":[188],"features":[189],"then":[191],"train":[194],"validate":[196],"Long":[197],"Short-Term":[198],"Memory":[199],"(LSTM)":[200],"models.":[201],"model":[204,216],"demonstrated":[205],"90%":[206],"accuracy":[207],"an":[209],"F1-score":[210],"0.89,":[212],"while":[213],"showed":[217],"a":[218],"mean":[219],"squared":[220],"error":[221],"(MSE)":[222],"1.1":[224],"test":[227],"set.":[228]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":3}],"updated_date":"2026-06-13T06:13:01.061226","created_date":"2025-10-10T00:00:00"}