{"id":"https://openalex.org/W4416144243","doi":"https://doi.org/10.1145/3721201.3721395","title":"Blood Pressure Estimation from Vibration Signals via Coarse-to-Fine Contrastive Learning, Feature Selection and Synthesis","display_name":"Blood Pressure Estimation from Vibration Signals via Coarse-to-Fine Contrastive Learning, Feature Selection and Synthesis","publication_year":2025,"publication_date":"2025-06-24","ids":{"openalex":"https://openalex.org/W4416144243","doi":"https://doi.org/10.1145/3721201.3721395"},"language":null,"primary_location":{"id":"doi:10.1145/3721201.3721395","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3721201.3721395","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3721201.3721395","source":null,"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":"Proceedings of the ACM/IEEE International Conference on Connected Health: Applications, Systems and Engineering Technologies","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://dl.acm.org/doi/pdf/10.1145/3721201.3721395","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5113418461","display_name":"Y. ZHANG","orcid":"https://orcid.org/0009-0000-5555-3778"},"institutions":[{"id":"https://openalex.org/I165733156","display_name":"University of Georgia","ror":"https://ror.org/00te3t702","country_code":"US","type":"education","lineage":["https://openalex.org/I165733156"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Yida Zhang","raw_affiliation_strings":["School of Electrical and Computer Engineering, University of Georgia, Athens, GA, USA"],"affiliations":[{"raw_affiliation_string":"School of Electrical and Computer Engineering, University of Georgia, Athens, GA, USA","institution_ids":["https://openalex.org/I165733156"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5087963259","display_name":"Jiayu Chen","orcid":"https://orcid.org/0009-0003-0904-4114"},"institutions":[{"id":"https://openalex.org/I165733156","display_name":"University of Georgia","ror":"https://ror.org/00te3t702","country_code":"US","type":"education","lineage":["https://openalex.org/I165733156"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jiayu Chen","raw_affiliation_strings":["School of Electrical and Computer Engineering, University of Georgia, Athens, GA, USA"],"affiliations":[{"raw_affiliation_string":"School of Electrical and Computer Engineering, University of Georgia, Athens, GA, USA","institution_ids":["https://openalex.org/I165733156"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102909478","display_name":"Yingjian Song","orcid":"https://orcid.org/0009-0005-5601-4465"},"institutions":[{"id":"https://openalex.org/I165733156","display_name":"University of Georgia","ror":"https://ror.org/00te3t702","country_code":"US","type":"education","lineage":["https://openalex.org/I165733156"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yingjian Song","raw_affiliation_strings":["School of Electrical and Computer Engineering, University of Georgia, Athens, GA, USA"],"affiliations":[{"raw_affiliation_string":"School of Electrical and Computer Engineering, University of Georgia, Athens, GA, USA","institution_ids":["https://openalex.org/I165733156"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5044289321","display_name":"Zixuan Zeng","orcid":"https://orcid.org/0009-0005-7051-7421"},"institutions":[{"id":"https://openalex.org/I165733156","display_name":"University of Georgia","ror":"https://ror.org/00te3t702","country_code":"US","type":"education","lineage":["https://openalex.org/I165733156"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Zixuan Zeng","raw_affiliation_strings":["School of Electrical and Computer Engineering, University of Georgia, Athens, GA, USA"],"affiliations":[{"raw_affiliation_string":"School of Electrical and Computer Engineering, University of Georgia, Athens, GA, USA","institution_ids":["https://openalex.org/I165733156"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100368805","display_name":"Xiang Zhang","orcid":"https://orcid.org/0000-0001-5097-2113"},"institutions":[{"id":"https://openalex.org/I102149020","display_name":"University of North Carolina at Charlotte","ror":"https://ror.org/04dawnj30","country_code":"US","type":"education","lineage":["https://openalex.org/I102149020"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Xiang Zhang","raw_affiliation_strings":["University of North Carolina at Charlotte, Charlotte, NC, USA"],"affiliations":[{"raw_affiliation_string":"University of North Carolina at Charlotte, Charlotte, NC, USA","institution_ids":["https://openalex.org/I102149020"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101509596","display_name":"Qin Lu","orcid":"https://orcid.org/0000-0002-4051-1396"},"institutions":[{"id":"https://openalex.org/I165733156","display_name":"University of Georgia","ror":"https://ror.org/00te3t702","country_code":"US","type":"education","lineage":["https://openalex.org/I165733156"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Qin Lu","raw_affiliation_strings":["School of Electrical and Computer Engineering, University of Georgia, Athens, GA, USA"],"affiliations":[{"raw_affiliation_string":"School of Electrical and Computer Engineering, University of Georgia, Athens, GA, USA","institution_ids":["https://openalex.org/I165733156"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5056825707","display_name":"Bradley G. Phillips","orcid":"https://orcid.org/0000-0003-1627-6549"},"institutions":[{"id":"https://openalex.org/I165733156","display_name":"University of Georgia","ror":"https://ror.org/00te3t702","country_code":"US","type":"education","lineage":["https://openalex.org/I165733156"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Bradley G. Phillips","raw_affiliation_strings":["University of Georgia, Athens, GA, USA"],"affiliations":[{"raw_affiliation_string":"University of Georgia, Athens, GA, USA","institution_ids":["https://openalex.org/I165733156"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5027931296","display_name":"Zaipeng Xie","orcid":"https://orcid.org/0000-0003-1637-1511"},"institutions":[{"id":"https://openalex.org/I163340411","display_name":"Hohai University","ror":"https://ror.org/01wd4xt90","country_code":"CN","type":"education","lineage":["https://openalex.org/I163340411"]},{"id":"https://openalex.org/I4210155611","display_name":"Ministry of Water Resources of the People's Republic of China","ror":"https://ror.org/04e698d63","country_code":"CN","type":"government","lineage":["https://openalex.org/I4210155611"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zaipeng Xie","raw_affiliation_strings":["Key Laboratory of Water Big Data Technology of Ministry of Water Resources, Hohai University, Nanjing, Jiangsu, China"],"affiliations":[{"raw_affiliation_string":"Key Laboratory of Water Big Data Technology of Ministry of Water Resources, Hohai University, Nanjing, Jiangsu, China","institution_ids":["https://openalex.org/I163340411","https://openalex.org/I4210155611"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5007530002","display_name":"Wen\u2010Zhan Song","orcid":"https://orcid.org/0000-0001-8174-1772"},"institutions":[{"id":"https://openalex.org/I165733156","display_name":"University of Georgia","ror":"https://ror.org/00te3t702","country_code":"US","type":"education","lineage":["https://openalex.org/I165733156"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Wenzhan Song","raw_affiliation_strings":["School of Electrical and Computer Engineering, University of Georgia, Athens, GA, USA"],"affiliations":[{"raw_affiliation_string":"School of Electrical and Computer Engineering, University of Georgia, Athens, GA, USA","institution_ids":["https://openalex.org/I165733156"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":9,"corresponding_author_ids":["https://openalex.org/A5113418461"],"corresponding_institution_ids":["https://openalex.org/I165733156"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.34394904,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"134","last_page":"138"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11196","display_name":"Non-Invasive Vital Sign Monitoring","score":0.8213000297546387,"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":0.8213000297546387,"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/T11021","display_name":"ECG Monitoring and Analysis","score":0.03480000048875809,"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/T11670","display_name":"Pressure Ulcer Prevention and Management","score":0.02539999969303608,"subfield":{"id":"https://openalex.org/subfields/3609","display_name":"Occupational Therapy"},"field":{"id":"https://openalex.org/fields/36","display_name":"Health Professions"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5861999988555908},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5841000080108643},{"id":"https://openalex.org/keywords/regularization","display_name":"Regularization (linguistics)","score":0.4902999997138977},{"id":"https://openalex.org/keywords/vibration","display_name":"Vibration","score":0.48510000109672546},{"id":"https://openalex.org/keywords/feature-selection","display_name":"Feature selection","score":0.4690000116825104},{"id":"https://openalex.org/keywords/signal","display_name":"SIGNAL (programming language)","score":0.41350001096725464},{"id":"https://openalex.org/keywords/domain","display_name":"Domain (mathematical analysis)","score":0.4074999988079071},{"id":"https://openalex.org/keywords/time-domain","display_name":"Time domain","score":0.39570000767707825},{"id":"https://openalex.org/keywords/range","display_name":"Range (aeronautics)","score":0.38100001215934753}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6711999773979187},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6395000219345093},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5861999988555908},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5841000080108643},{"id":"https://openalex.org/C2776135515","wikidata":"https://www.wikidata.org/wiki/Q17143721","display_name":"Regularization (linguistics)","level":2,"score":0.4902999997138977},{"id":"https://openalex.org/C198394728","wikidata":"https://www.wikidata.org/wiki/Q3695508","display_name":"Vibration","level":2,"score":0.48510000109672546},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.4690000116825104},{"id":"https://openalex.org/C2779843651","wikidata":"https://www.wikidata.org/wiki/Q7390335","display_name":"SIGNAL (programming language)","level":2,"score":0.41350001096725464},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.4074999988079071},{"id":"https://openalex.org/C103824480","wikidata":"https://www.wikidata.org/wiki/Q185889","display_name":"Time domain","level":2,"score":0.39570000767707825},{"id":"https://openalex.org/C204323151","wikidata":"https://www.wikidata.org/wiki/Q905424","display_name":"Range (aeronautics)","level":2,"score":0.38100001215934753},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.38029998540878296},{"id":"https://openalex.org/C84393581","wikidata":"https://www.wikidata.org/wiki/Q82642","display_name":"Blood pressure","level":2,"score":0.36559998989105225},{"id":"https://openalex.org/C19118579","wikidata":"https://www.wikidata.org/wiki/Q786423","display_name":"Frequency domain","level":2,"score":0.36000001430511475},{"id":"https://openalex.org/C94915269","wikidata":"https://www.wikidata.org/wiki/Q1834857","display_name":"Detector","level":2,"score":0.3571999967098236},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.3495999872684479},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.3425000011920929},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.33970001339912415},{"id":"https://openalex.org/C104267543","wikidata":"https://www.wikidata.org/wiki/Q208163","display_name":"Signal processing","level":3,"score":0.2935999929904938},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.28679999709129333},{"id":"https://openalex.org/C207685749","wikidata":"https://www.wikidata.org/wiki/Q2088941","display_name":"Domain knowledge","level":2,"score":0.2849000096321106},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.27900001406669617},{"id":"https://openalex.org/C37616216","wikidata":"https://www.wikidata.org/wiki/Q3218363","display_name":"Lasso (programming language)","level":2,"score":0.2736000120639801},{"id":"https://openalex.org/C188154048","wikidata":"https://www.wikidata.org/wiki/Q6803609","display_name":"Mean absolute error","level":3,"score":0.2703999876976013},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.26269999146461487},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.25609999895095825},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.25220000743865967}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3721201.3721395","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3721201.3721395","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3721201.3721395","source":null,"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":"Proceedings of the ACM/IEEE International Conference on Connected Health: Applications, Systems and Engineering Technologies","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3721201.3721395","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3721201.3721395","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3721201.3721395","source":null,"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":"Proceedings of the ACM/IEEE International Conference on Connected Health: Applications, Systems and Engineering Technologies","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4416144243.pdf","grobid_xml":"https://content.openalex.org/works/W4416144243.grobid-xml"},"referenced_works_count":17,"referenced_works":["https://openalex.org/W2135046866","https://openalex.org/W2182353144","https://openalex.org/W2752320209","https://openalex.org/W2808955427","https://openalex.org/W2884446700","https://openalex.org/W2972810968","https://openalex.org/W3012919764","https://openalex.org/W3084430883","https://openalex.org/W3114582221","https://openalex.org/W3138183469","https://openalex.org/W3202364339","https://openalex.org/W4234698323","https://openalex.org/W4311664146","https://openalex.org/W4312303088","https://openalex.org/W4396218572","https://openalex.org/W4404034561","https://openalex.org/W6888435829"],"related_works":[],"abstract_inverted_index":{"Effective":[0],"blood":[1,34,75,188,217],"pressure":[2,35,76,189,218],"estimation":[3,77],"during":[4],"sleeping":[5],"is":[6,99,119,158],"crucial":[7],"for":[8,33],"the":[9,129,141,176,181,206,222,229,233],"monitoring":[10],"of":[11,15,22,131],"a":[12,92,135,147,151],"wide":[13],"range":[14],"diseases.":[16],"In":[17,68,220],"recent":[18],"years,":[19],"different":[20,126],"types":[21],"vibration":[23,94,105],"signals":[24,106],"induced":[25],"by":[26,108,232],"cardiac":[27,109],"activities":[28],"have":[29],"been":[30],"widely":[31],"studied":[32],"estimation.":[36,190,219],"However,":[37],"most":[38],"existing":[39],"methods":[40],"either":[41],"rely":[42],"on":[43,97,194,216],"manual":[44],"feature":[45,149],"identification,":[46],"requiring":[47],"extensive":[48,52],"physiological":[49,144],"expertise":[50],"and":[51,87,183,196,209,235],"experiments,":[53],"or":[54],"employ":[55],"deep":[56],"learning":[57,133],"(DL)":[58],"models,":[59],"which":[60,164],"struggle":[61],"with":[62,134,155,171,228],"domain":[63],"shifts":[64],"in":[65,226],"temporal":[66],"signals.":[67],"this":[69],"paper,":[70],"we":[71],"propose":[72],"an":[73,112],"innovative":[74,113],"framework":[78],"called":[79],"CLFSS,":[80,90],"using":[81],"coarse-to-fine":[82],"Contrastive":[83,115],"Learning,":[84],"Feature":[85],"Selection":[86],"Synthesis.":[88],"Within":[89],"BedDot,":[91],"custom-designed":[93],"detector":[95],"based":[96],"seismometer,":[98],"first":[100],"used":[101],"to":[102,121,139,160,168],"capture":[103],"subtle":[104],"generated":[107],"activities.":[110],"Then":[111],"Coarse-to-Fine":[114],"Learning":[116],"(CFCL)":[117],"module":[118],"designed":[120],"generate":[122],"generalizable":[123],"representations":[124,182],"across":[125],"granularities,":[127],"leveraging":[128],"principles":[130],"contrastive":[132],"divide-and-conquer":[136],"strategy.":[137],"Meanwhile,":[138],"utilize":[140],"potentially":[142],"relevant":[143],"features":[145,170,208],"from":[146],"large":[148],"set,":[150],"one-to-one":[152],"connected":[153],"layer":[154],"Lasso":[156],"regularization":[157],"created":[159],"reveal":[161],"related":[162,207],"features,":[163,185],"are":[165,192,225],"then":[166],"synthesized":[167,184],"form":[169],"high-level":[172],"semantic":[173],"information.":[174],"Finally,":[175],"proposed":[177],"CLFSS":[178,202],"effectively":[179],"aggregates":[180],"achieving":[186],"accurate":[187],"Experiments":[191],"conducted":[193],"public":[195],"private":[197],"datasets,":[198],"clearly":[199],"demonstrating":[200,239],"that":[201],"can":[203],"accurately":[204],"select":[205],"achieve":[210],"low":[211],"Mean":[212],"Absolute":[213],"Error":[214],"(MAE)":[215],"addition,":[221],"experimental":[223],"results":[224],"compliance":[227],"standards":[230],"established":[231],"Food":[234],"Drug":[236],"Administration":[237],"(FDA),":[238],"significant":[240],"real-world":[241],"potential.":[242]},"counts_by_year":[],"updated_date":"2026-03-12T06:13:28.667946","created_date":"2025-11-12T00:00:00"}