{"id":"https://openalex.org/W4392903021","doi":"https://doi.org/10.1109/icassp48485.2024.10446054","title":"Freq2Time: Weakly Supervised Learning of Camera-Based RPPG from Heart Rate","display_name":"Freq2Time: Weakly Supervised Learning of Camera-Based RPPG from Heart Rate","publication_year":2024,"publication_date":"2024-03-18","ids":{"openalex":"https://openalex.org/W4392903021","doi":"https://doi.org/10.1109/icassp48485.2024.10446054"},"language":"en","primary_location":{"id":"doi:10.1109/icassp48485.2024.10446054","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp48485.2024.10446054","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"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/A5089334180","display_name":"Jeremy Speth","orcid":"https://orcid.org/0000-0002-8911-6063"},"institutions":[{"id":"https://openalex.org/I107639228","display_name":"University of Notre Dame","ror":"https://ror.org/00mkhxb43","country_code":"US","type":"education","lineage":["https://openalex.org/I107639228"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jeremy Speth","raw_affiliation_strings":["University of Notre,Computer Science & Engineering,Dame,USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Notre,Computer Science & Engineering,Dame,USA","institution_ids":["https://openalex.org/I107639228"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5051782097","display_name":"Korosh Vatanparvar","orcid":"https://orcid.org/0000-0002-9089-0021"},"institutions":[{"id":"https://openalex.org/I2250650973","display_name":"Samsung (South Korea)","ror":"https://ror.org/04w3jy968","country_code":"KR","type":"company","lineage":["https://openalex.org/I2250650973"]},{"id":"https://openalex.org/I4210101778","display_name":"Samsung (United States)","ror":"https://ror.org/01bfbvm65","country_code":"US","type":"company","lineage":["https://openalex.org/I2250650973","https://openalex.org/I4210101778"]}],"countries":["KR","US"],"is_corresponding":false,"raw_author_name":"Korosh Vatanparvar","raw_affiliation_strings":["Samsung Research America,Digital Health Lab,USA","Digital Health Lab, Samsung Research America, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Samsung Research America,Digital Health Lab,USA","institution_ids":["https://openalex.org/I2250650973"]},{"raw_affiliation_string":"Digital Health Lab, Samsung Research America, USA","institution_ids":["https://openalex.org/I4210101778"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5069732505","display_name":"Li Zhu","orcid":"https://orcid.org/0000-0001-6373-5846"},"institutions":[{"id":"https://openalex.org/I2250650973","display_name":"Samsung (South Korea)","ror":"https://ror.org/04w3jy968","country_code":"KR","type":"company","lineage":["https://openalex.org/I2250650973"]},{"id":"https://openalex.org/I4210101778","display_name":"Samsung (United States)","ror":"https://ror.org/01bfbvm65","country_code":"US","type":"company","lineage":["https://openalex.org/I2250650973","https://openalex.org/I4210101778"]}],"countries":["KR","US"],"is_corresponding":false,"raw_author_name":"Li Zhu","raw_affiliation_strings":["Samsung Research America,Digital Health Lab,USA","Digital Health Lab, Samsung Research America, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Samsung Research America,Digital Health Lab,USA","institution_ids":["https://openalex.org/I2250650973"]},{"raw_affiliation_string":"Digital Health Lab, Samsung Research America, USA","institution_ids":["https://openalex.org/I4210101778"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5002167339","display_name":"Jilong Kuang","orcid":"https://orcid.org/0000-0003-2942-9102"},"institutions":[{"id":"https://openalex.org/I2250650973","display_name":"Samsung (South Korea)","ror":"https://ror.org/04w3jy968","country_code":"KR","type":"company","lineage":["https://openalex.org/I2250650973"]},{"id":"https://openalex.org/I4210101778","display_name":"Samsung (United States)","ror":"https://ror.org/01bfbvm65","country_code":"US","type":"company","lineage":["https://openalex.org/I2250650973","https://openalex.org/I4210101778"]}],"countries":["KR","US"],"is_corresponding":false,"raw_author_name":"Jilong Kuang","raw_affiliation_strings":["Samsung Research America,Digital Health Lab,USA","Digital Health Lab, Samsung Research America, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Samsung Research America,Digital Health Lab,USA","institution_ids":["https://openalex.org/I2250650973"]},{"raw_affiliation_string":"Digital Health Lab, Samsung Research America, USA","institution_ids":["https://openalex.org/I4210101778"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5036964136","display_name":"Alex Gao","orcid":"https://orcid.org/0000-0002-3393-5043"},"institutions":[{"id":"https://openalex.org/I2250650973","display_name":"Samsung (South Korea)","ror":"https://ror.org/04w3jy968","country_code":"KR","type":"company","lineage":["https://openalex.org/I2250650973"]},{"id":"https://openalex.org/I4210101778","display_name":"Samsung (United States)","ror":"https://ror.org/01bfbvm65","country_code":"US","type":"company","lineage":["https://openalex.org/I2250650973","https://openalex.org/I4210101778"]}],"countries":["KR","US"],"is_corresponding":false,"raw_author_name":"Alex Gao","raw_affiliation_strings":["Samsung Research America,Digital Health Lab,USA","Digital Health Lab, Samsung Research America, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Samsung Research America,Digital Health Lab,USA","institution_ids":["https://openalex.org/I2250650973"]},{"raw_affiliation_string":"Digital Health Lab, Samsung Research America, USA","institution_ids":["https://openalex.org/I4210101778"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":5,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.8139,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":{"value":0.67535943,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":95,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"6880","last_page":"6884"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11196","display_name":"Non-Invasive Vital Sign Monitoring","score":1.0,"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"}},"topics":[{"id":"https://openalex.org/T11196","display_name":"Non-Invasive Vital Sign Monitoring","score":1.0,"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/T10745","display_name":"Heart Rate Variability and Autonomic Control","score":0.996999979019165,"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/T11700","display_name":"Hemodynamic Monitoring and Therapy","score":0.9969000220298767,"subfield":{"id":"https://openalex.org/subfields/2746","display_name":"Surgery"},"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/photoplethysmogram","display_name":"Photoplethysmogram","score":0.8157106637954712},{"id":"https://openalex.org/keywords/ground-truth","display_name":"Ground truth","score":0.774261474609375},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7374141812324524},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6861010789871216},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6315186023712158},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4926963150501251},{"id":"https://openalex.org/keywords/supervised-learning","display_name":"Supervised learning","score":0.4142102599143982},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3950977325439453},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.3892270624637604},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.36268699169158936},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.339791476726532},{"id":"https://openalex.org/keywords/filter","display_name":"Filter (signal processing)","score":0.09825700521469116}],"concepts":[{"id":"https://openalex.org/C116390426","wikidata":"https://www.wikidata.org/wiki/Q7187885","display_name":"Photoplethysmogram","level":3,"score":0.8157106637954712},{"id":"https://openalex.org/C146849305","wikidata":"https://www.wikidata.org/wiki/Q370766","display_name":"Ground truth","level":2,"score":0.774261474609375},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7374141812324524},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6861010789871216},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6315186023712158},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4926963150501251},{"id":"https://openalex.org/C136389625","wikidata":"https://www.wikidata.org/wiki/Q334384","display_name":"Supervised learning","level":3,"score":0.4142102599143982},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3950977325439453},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.3892270624637604},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.36268699169158936},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.339791476726532},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.09825700521469116}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icassp48485.2024.10446054","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp48485.2024.10446054","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":25,"referenced_works":["https://openalex.org/W2003922338","https://openalex.org/W2008821584","https://openalex.org/W2044722977","https://openalex.org/W2066932506","https://openalex.org/W2069692225","https://openalex.org/W2162273778","https://openalex.org/W2520509592","https://openalex.org/W2553576544","https://openalex.org/W2787182113","https://openalex.org/W2893758214","https://openalex.org/W2963433879","https://openalex.org/W2982196965","https://openalex.org/W3036299321","https://openalex.org/W3137020077","https://openalex.org/W3199809247","https://openalex.org/W3204093119","https://openalex.org/W4288327876","https://openalex.org/W4302011732","https://openalex.org/W4312358294","https://openalex.org/W4312829251","https://openalex.org/W4372341893","https://openalex.org/W4386076620","https://openalex.org/W6764045775","https://openalex.org/W6767419198","https://openalex.org/W6845429721"],"related_works":["https://openalex.org/W2732360296","https://openalex.org/W4226493464","https://openalex.org/W4312417841","https://openalex.org/W3133861977","https://openalex.org/W2951211570","https://openalex.org/W3103566983","https://openalex.org/W3029198973","https://openalex.org/W4386076228","https://openalex.org/W4310825149","https://openalex.org/W2798269247"],"abstract_inverted_index":{"Camera-based":[0],"pulse":[1],"measurements":[2],"from":[3,129],"remote":[4],"photoplethysmography":[5],"(rPPG)":[6],"have":[7],"rapidly":[8],"improved":[9],"over":[10,95],"recent":[11],"years":[12],"due":[13],"to":[14,63],"innovations":[15],"in":[16],"video":[17,47,127],"processing":[18],"and":[19,48,89],"deep":[20],"learning.":[21],"However,":[22],"modern":[23],"data-driven":[24],"solutions":[25],"require":[26],"large":[27],"training":[28,36],"datasets":[29],"collected":[30],"under":[31],"diverse":[32],"conditions.":[33],"Collecting":[34],"such":[35,146],"data":[37,128],"is":[38],"made":[39],"more":[40],"challenging":[41,125],"by":[42],"the":[43,73,84,91,110],"need":[44,74],"for":[45,75,142],"time-synchronized":[46],"physiological":[49,144],"signals":[50],"as":[51,80,147],"ground":[52,81],"truth.":[53],"This":[54],"paper":[55],"presents":[56],"a":[57,96],"weakly":[58],"supervised":[59],"learning":[60],"framework,":[61],"Freq2Time,":[62],"train":[64],"with":[65,109,117,124],"heart":[66,148],"rate":[67,149],"(HR)":[68],"labels.":[69],"Our":[70],"framework":[71,112],"mitigates":[72],"simultaneous":[76],"PPG":[77],"or":[78],"ECG":[79],"truth,":[82],"since":[83],"HR":[85,115],"changes":[86],"relatively":[87],"slowly":[88],"describes":[90],"target":[92],"rPPG":[93,138],"signal":[94],"time":[97,139],"interval.":[98],"We":[99],"show":[100],"that":[101],"3D":[102],"convolutional":[103],"neural":[104],"network":[105],"(3DCNN)":[106],"models":[107,134],"trained":[108],"Freq2Time":[111],"give":[113],"state-of-the-art":[114],"performance":[116],"MAE":[118],"of":[119],"2.86":[120],"bpm,":[121],"when":[122],"tested":[123],"smartphone":[126],"30":[130],"subjects.":[131],"Additionally,":[132],"our":[133],"still":[135],"learn":[136],"accurate":[137],"signals,":[140],"allowing":[141],"other":[143],"metrics":[145],"variability.":[150]},"counts_by_year":[{"year":2026,"cited_by_count":3},{"year":2025,"cited_by_count":2}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}