{"id":"https://openalex.org/W4315573371","doi":"https://doi.org/10.3390/s23020826","title":"Machine Learning Models for the Automatic Detection of Exercise Thresholds in Cardiopulmonary Exercising Tests: From Regression to Generation to Explanation","display_name":"Machine Learning Models for the Automatic Detection of Exercise Thresholds in Cardiopulmonary Exercising Tests: From Regression to Generation to Explanation","publication_year":2023,"publication_date":"2023-01-11","ids":{"openalex":"https://openalex.org/W4315573371","doi":"https://doi.org/10.3390/s23020826","pmid":"https://pubmed.ncbi.nlm.nih.gov/36679622"},"language":"en","primary_location":{"id":"doi:10.3390/s23020826","is_oa":true,"landing_page_url":"https://doi.org/10.3390/s23020826","pdf_url":"https://www.mdpi.com/1424-8220/23/2/826/pdf?version=1673419442","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/1424-8220/23/2/826/pdf?version=1673419442","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5039322808","display_name":"Andrea Zignoli","orcid":"https://orcid.org/0000-0003-1315-5573"},"institutions":[{"id":"https://openalex.org/I193223587","display_name":"University of Trento","ror":"https://ror.org/05trd4x28","country_code":"IT","type":"education","lineage":["https://openalex.org/I193223587"]}],"countries":["IT"],"is_corresponding":true,"raw_author_name":"Andrea Zignoli","raw_affiliation_strings":["Department of Industrial Engineering, University of Trento, 38123 Trento, Italy"],"raw_orcid":"https://orcid.org/0000-0003-1315-5573","affiliations":[{"raw_affiliation_string":"Department of Industrial Engineering, University of Trento, 38123 Trento, Italy","institution_ids":["https://openalex.org/I193223587"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5039322808"],"corresponding_institution_ids":["https://openalex.org/I193223587"],"apc_list":{"value":2400,"currency":"CHF","value_usd":2598},"apc_paid":{"value":2400,"currency":"CHF","value_usd":2598},"fwci":3.9316,"has_fulltext":true,"cited_by_count":21,"citation_normalized_percentile":{"value":0.94627587,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":96,"max":99},"biblio":{"volume":"23","issue":"2","first_page":"826","last_page":"826"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9916999936103821,"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"}},"topics":[{"id":"https://openalex.org/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9916999936103821,"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"}},{"id":"https://openalex.org/T12026","display_name":"Explainable Artificial Intelligence (XAI)","score":0.9779999852180481,"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"}},{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9585000276565552,"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/machine-learning","display_name":"Machine learning","score":0.7685798406600952},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7192765474319458},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6149704456329346},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.5904259085655212},{"id":"https://openalex.org/keywords/regression","display_name":"Regression","score":0.529226541519165},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5144627094268799},{"id":"https://openalex.org/keywords/variable","display_name":"Variable (mathematics)","score":0.49797892570495605},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.4669063687324524},{"id":"https://openalex.org/keywords/regression-analysis","display_name":"Regression analysis","score":0.4562372863292694},{"id":"https://openalex.org/keywords/linear-regression","display_name":"Linear regression","score":0.42963314056396484},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.18274632096290588},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.15486568212509155}],"concepts":[{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.7685798406600952},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7192765474319458},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6149704456329346},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.5904259085655212},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.529226541519165},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5144627094268799},{"id":"https://openalex.org/C182365436","wikidata":"https://www.wikidata.org/wiki/Q50701","display_name":"Variable (mathematics)","level":2,"score":0.49797892570495605},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.4669063687324524},{"id":"https://openalex.org/C152877465","wikidata":"https://www.wikidata.org/wiki/Q208042","display_name":"Regression analysis","level":2,"score":0.4562372863292694},{"id":"https://openalex.org/C48921125","wikidata":"https://www.wikidata.org/wiki/Q10861030","display_name":"Linear regression","level":2,"score":0.42963314056396484},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.18274632096290588},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.15486568212509155},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0}],"mesh":[{"descriptor_ui":"D000069550","descriptor_name":"Machine Learning","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D000069550","descriptor_name":"Machine Learning","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D000069550","descriptor_name":"Machine Learning","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D000465","descriptor_name":"Algorithms","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D000465","descriptor_name":"Algorithms","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D000465","descriptor_name":"Algorithms","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D005080","descriptor_name":"Exercise Test","qualifier_ui":"Q000379","qualifier_name":"methods","is_major_topic":false},{"descriptor_ui":"D005080","descriptor_name":"Exercise Test","qualifier_ui":"Q000379","qualifier_name":"methods","is_major_topic":false},{"descriptor_ui":"D005080","descriptor_name":"Exercise Test","qualifier_ui":"Q000379","qualifier_name":"methods","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}],"locations_count":5,"locations":[{"id":"doi:10.3390/s23020826","is_oa":true,"landing_page_url":"https://doi.org/10.3390/s23020826","pdf_url":"https://www.mdpi.com/1424-8220/23/2/826/pdf?version=1673419442","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:36679622","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/36679622","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:pubmedcentral.nih.gov:9867502","is_oa":true,"landing_page_url":"https://www.ncbi.nlm.nih.gov/pmc/articles/9867502","pdf_url":"https://pmc.ncbi.nlm.nih.gov/articles/PMC9867502/pdf/sensors-23-00826.pdf","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"},{"id":"pmh:oai:doaj.org/article:f48107afbbbf4fa582c81c943543d4be","is_oa":true,"landing_page_url":"https://doaj.org/article/f48107afbbbf4fa582c81c943543d4be","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 23, Iss 2, p 826 (2023)","raw_type":"article"},{"id":"pmh:oai:mdpi.com:/1424-8220/23/2/826/","is_oa":true,"landing_page_url":"https://dx.doi.org/10.3390/s23020826","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 23; Issue 2; Pages: 826","raw_type":"Text"}],"best_oa_location":{"id":"doi:10.3390/s23020826","is_oa":true,"landing_page_url":"https://doi.org/10.3390/s23020826","pdf_url":"https://www.mdpi.com/1424-8220/23/2/826/pdf?version=1673419442","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":false},"content_urls":{"pdf":"https://content.openalex.org/works/W4315573371.pdf"},"referenced_works_count":37,"referenced_works":["https://openalex.org/W1527649914","https://openalex.org/W1971916086","https://openalex.org/W1975919407","https://openalex.org/W1987924400","https://openalex.org/W2006364011","https://openalex.org/W2026430219","https://openalex.org/W2086725891","https://openalex.org/W2103421273","https://openalex.org/W2127871157","https://openalex.org/W2145139856","https://openalex.org/W2149394968","https://openalex.org/W2275086416","https://openalex.org/W2794631799","https://openalex.org/W2891676555","https://openalex.org/W2892035503","https://openalex.org/W2892741787","https://openalex.org/W2908201961","https://openalex.org/W2919115771","https://openalex.org/W2921763762","https://openalex.org/W2922199635","https://openalex.org/W2942858056","https://openalex.org/W2962858109","https://openalex.org/W3096831136","https://openalex.org/W3112896447","https://openalex.org/W3168933482","https://openalex.org/W3211266550","https://openalex.org/W4210950012","https://openalex.org/W4213136040","https://openalex.org/W4220700957","https://openalex.org/W4220898666","https://openalex.org/W4226352480","https://openalex.org/W4226467611","https://openalex.org/W4226507083","https://openalex.org/W4251858601","https://openalex.org/W4281635259","https://openalex.org/W4292975069","https://openalex.org/W4295422516"],"related_works":["https://openalex.org/W2610868774","https://openalex.org/W4244900409","https://openalex.org/W2362588090","https://openalex.org/W4399767649","https://openalex.org/W2092994918","https://openalex.org/W3216594821","https://openalex.org/W2390006526","https://openalex.org/W31220157","https://openalex.org/W3215700490","https://openalex.org/W1915333409"],"abstract_inverted_index":{"The":[0,108,151],"cardiopulmonary":[1],"exercise":[2,47,72,187],"test":[3],"(CPET)":[4],"constitutes":[5],"a":[6,87,117,127,168,199,244],"gold":[7],"standard":[8],"for":[9,21,113,122,153,160,171],"the":[10,22,30,41,46,56,66,71,82,101,104,154,172,184,215,218,226,234,264],"assessment":[11],"of":[12,24,33,37,44,68,84,103,139,186,217,222,257],"an":[13,97,180,202,212],"individual's":[14],"cardiovascular":[15],"fitness.":[16],"A":[17],"trend":[18],"is":[19],"emerging":[20],"development":[23],"new":[25],"machine-learning":[26],"techniques":[27,159,178],"applied":[28],"to":[29,195,228,259],"automatic":[31],"process":[32,67,83],"CPET":[34,88,162,265],"data.":[35],"Some":[36],"these":[38],"focus":[39],"on":[40],"precise":[42],"task":[43],"detecting":[45],"thresholds,":[48],"which":[49],"represent":[50],"important":[51],"physiological":[52],"parameters.":[53],"Three":[54],"are":[55,164,192],"major":[57,260],"challenges":[58],"tackled":[59],"by":[60],"this":[61,238],"contribution:":[62],"(A)":[63,116,175],"regression":[64],"(i.e.,":[65,81,95],"correctly":[69],"identifying":[70],"intensity":[73,188],"domains":[74],"and":[75,92,133,137,157,206,268],"their":[76,230],"crossing":[77],"points);":[78],"(B)":[79,126,190],"generation":[80],"artificially":[85,203],"creating":[86],"data":[89],"file":[90],"ex-novo);":[91],"(C)":[93,134,207],"explanation":[94,99,213],"proving":[96],"interpretable":[98],"about":[100,214],"output":[102],"machine":[105,176],"learning":[106,177],"model).":[107],"following":[109],"methods":[110],"were":[111],"used":[112],"each":[114],"challenge:":[115],"convolutional":[118],"neural":[119,131],"network":[120],"adapted":[121],"multi-variable":[123],"time":[124],"series;":[125],"conditional":[128],"generative":[129],"adversarial":[130],"network;":[132],"visual":[135],"explanations":[136],"calculations":[138],"model":[140],"decisions":[141],"have":[142,240],"been":[143,241],"conducted":[144],"using":[145],"cooperative":[146],"game":[147],"theory":[148],"(Shapley's":[149],"values).":[150],"results":[152],"regression,":[155],"generation,":[156],"explanatory":[158],"AI-assisted":[161],"interpretation":[163],"presented":[165],"here":[166],"in":[167,183,220,237,263],"unique":[169],"framework":[170],"first":[173],"time:":[174],"reported":[179],"expert-level":[181],"accuracy":[182],"classification":[185],"domains;":[189],"experts":[191],"not":[193],"able":[194],"substantially":[196],"differentiate":[197],"between":[198],"real":[200],"vs":[201],"generated":[204],"CPET;":[205],"Shapley's":[208],"values":[209],"can":[210],"provide":[211],"choices":[216],"algorithms":[219],"terms":[221],"ventilatory":[223],"variables.":[224],"With":[225],"aim":[227],"increase":[229],"technology-readiness":[231],"level,":[232],"all":[233],"models":[235],"discussed":[236],"contribution":[239,253],"incorporated":[242],"into":[243],"free-to-use":[245],"Python":[246],"package":[247],"called":[248],"pyoxynet":[249],"(ver.":[250],"12.1).":[251],"This":[252],"should":[254],"therefore":[255],"be":[256],"interest":[258],"players":[261],"operating":[262],"device":[266],"market":[267],"engineering.":[269]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":8},{"year":2024,"cited_by_count":8},{"year":2023,"cited_by_count":3}],"updated_date":"2026-05-21T06:26:12.895304","created_date":"2025-10-10T00:00:00"}