{"id":"https://openalex.org/W4225147670","doi":"https://doi.org/10.48550/arxiv.2204.13597","title":"PhysioGAN: Training High Fidelity Generative Model for Physiological Sensor Readings","display_name":"PhysioGAN: Training High Fidelity Generative Model for Physiological Sensor Readings","publication_year":2022,"publication_date":"2022-04-25","ids":{"openalex":"https://openalex.org/W4225147670","doi":"https://doi.org/10.48550/arxiv.2204.13597"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2204.13597","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2204.13597","pdf_url":"https://arxiv.org/pdf/2204.13597","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},"type":"preprint","indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2204.13597","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5034311510","display_name":"Moustafa Alzantot","orcid":"https://orcid.org/0000-0003-2614-9877"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Alzantot, Moustafa","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100645570","display_name":"Luis Garc\u00eda","orcid":"https://orcid.org/0009-0002-5225-1787"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Garcia, Luis","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5074563122","display_name":"Mani Srivastava","orcid":"https://orcid.org/0000-0002-3782-9192"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Srivastava, Mani","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5034311510"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9495000243186951,"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"}},"topics":[{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9495000243186951,"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/T10812","display_name":"Human Pose and Action Recognition","score":0.9452999830245972,"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"}},{"id":"https://openalex.org/T10444","display_name":"Context-Aware Activity Recognition Systems","score":0.9233999848365784,"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/computer-science","display_name":"Computer science","score":0.7479041218757629},{"id":"https://openalex.org/keywords/discriminator","display_name":"Discriminator","score":0.6821027994155884},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6578621864318848},{"id":"https://openalex.org/keywords/generative-grammar","display_name":"Generative grammar","score":0.6199650764465332},{"id":"https://openalex.org/keywords/generative-model","display_name":"Generative model","score":0.6023542284965515},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5233025550842285},{"id":"https://openalex.org/keywords/autoencoder","display_name":"Autoencoder","score":0.5070178508758545},{"id":"https://openalex.org/keywords/fidelity","display_name":"Fidelity","score":0.5054304599761963},{"id":"https://openalex.org/keywords/discriminative-model","display_name":"Discriminative model","score":0.5047708749771118},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.47772809863090515},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.43722596764564514},{"id":"https://openalex.org/keywords/synthetic-data","display_name":"Synthetic data","score":0.4277951717376709},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.4154837727546692},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.23613190650939941}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7479041218757629},{"id":"https://openalex.org/C2779803651","wikidata":"https://www.wikidata.org/wiki/Q5282088","display_name":"Discriminator","level":3,"score":0.6821027994155884},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6578621864318848},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.6199650764465332},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.6023542284965515},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5233025550842285},{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.5070178508758545},{"id":"https://openalex.org/C2776459999","wikidata":"https://www.wikidata.org/wiki/Q2119376","display_name":"Fidelity","level":2,"score":0.5054304599761963},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.5047708749771118},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.47772809863090515},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.43722596764564514},{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.4277951717376709},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.4154837727546692},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.23613190650939941},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0},{"id":"https://openalex.org/C94915269","wikidata":"https://www.wikidata.org/wiki/Q1834857","display_name":"Detector","level":2,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2204.13597","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2204.13597","pdf_url":"https://arxiv.org/pdf/2204.13597","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},{"id":"doi:10.48550/arxiv.2204.13597","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2204.13597","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2204.13597","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2204.13597","pdf_url":"https://arxiv.org/pdf/2204.13597","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/10","score":0.5600000023841858,"display_name":"Reduced inequalities"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W4309969736","https://openalex.org/W4394785709","https://openalex.org/W4384517922","https://openalex.org/W4377088509","https://openalex.org/W2953501176","https://openalex.org/W2965095304","https://openalex.org/W2470043383","https://openalex.org/W3034474024","https://openalex.org/W2770818364","https://openalex.org/W4317242571"],"abstract_inverted_index":{"Generative":[0],"models":[1,127,160,165,187],"such":[2],"as":[3],"the":[4,9,21,37,104,125,130,138,145,190,196],"variational":[5],"autoencoder":[6],"(VAE)":[7],"and":[8,30,41,97,114,141,177],"generative":[10,80,159],"adversarial":[11],"networks":[12],"(GAN)":[13],"have":[14,71,174],"proven":[15],"to":[16,55,82,124,185],"be":[17],"incredibly":[18],"powerful":[19],"for":[20,59,200],"generation":[22,135],"of":[23,32,39,53,93,132,144,198],"synthetic":[24,86,146,169],"data":[25,89,170,202],"that":[26,150,163],"preserves":[27],"statistical":[28],"properties":[29],"utility":[31,156],"real-world":[33,110],"datasets,":[34],"especially":[35],"in":[36,68,180,204],"context":[38,70],"image":[40],"natural":[42],"language":[43],"text.":[44],"Nevertheless,":[45],"until":[46],"now,":[47],"there":[48],"has":[49],"no":[50],"successful":[51],"demonstration":[52],"how":[54],"apply":[56],"either":[57],"method":[58],"generating":[60],"useful":[61],"physiological":[62,87],"sensory":[63],"data.":[64,192],"The":[65],"state-of-the-art":[66,105],"techniques":[67,106],"this":[69],"achieved":[72],"only":[73,129,168,175],"limited":[74],"success.":[75],"We":[76,100,121,148],"present":[77],"PHYSIOGAN,":[78],"a":[79,98],"model":[81],"produce":[83],"high":[84],"fidelity":[85],"sensor":[88,201],"readings.":[90],"PHYSIOGAN":[91,102,123,151,173,199],"consists":[92],"an":[94],"encoder,":[95],"decoder,":[96],"discriminator.":[99],"evaluate":[101],"against":[103],"using":[107],"two":[108],"different":[109],"datasets:":[111],"ECG":[112],"classification":[113,164,182,186],"activity":[115],"recognition":[116],"from":[117],"motion":[118],"sensors":[119],"datasets.":[120,147],"compare":[122],"baseline":[126],"not":[128],"accuracy":[131,183],"class":[133],"conditional":[134],"but":[136],"also":[137],"sample":[139,142],"diversity":[140],"novelty":[143],"prove":[149],"generates":[152],"samples":[153],"with":[154],"higher":[155],"than":[157],"other":[158],"by":[161,172],"showing":[162],"trained":[166,188],"on":[167,189],"generated":[171],"10%":[176],"20%":[178],"decrease":[179],"their":[181],"relative":[184],"real":[191],"Furthermore,":[193],"we":[194],"demonstrate":[195],"use":[197],"imputation":[203],"creating":[205],"plausible":[206],"results.":[207]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1}],"updated_date":"2026-02-09T09:26:11.010843","created_date":"2022-05-01T00:00:00"}
