{"id":"https://openalex.org/W4323521033","doi":"https://doi.org/10.1109/ieeeconf56349.2022.10052019","title":"Topological Knowledge Distillation for Wearable Sensor Data","display_name":"Topological Knowledge Distillation for Wearable Sensor Data","publication_year":2022,"publication_date":"2022-10-31","ids":{"openalex":"https://openalex.org/W4323521033","doi":"https://doi.org/10.1109/ieeeconf56349.2022.10052019","pmid":"https://pubmed.ncbi.nlm.nih.gov/37583442"},"language":"en","primary_location":{"id":"doi:10.1109/ieeeconf56349.2022.10052019","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/ieeeconf56349.2022.10052019","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 56th Asilomar Conference on Signals, Systems, and Computers","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref","pubmed"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://pmc.ncbi.nlm.nih.gov/articles/PMC10426276/pdf/nihms-1920709.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5003097277","display_name":"Eun Som Jeon","orcid":"https://orcid.org/0000-0002-1112-4653"},"institutions":[{"id":"https://openalex.org/I55732556","display_name":"Arizona State University","ror":"https://ror.org/03efmqc40","country_code":"US","type":"education","lineage":["https://openalex.org/I55732556"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Eun Som Jeon","raw_affiliation_strings":["School of Arts, Media and Engineering and School of Electrical, Computer and Energy Engineering, Arizona State University,Geometric Media Lab,Tempe,AZ,USA,85281"],"affiliations":[{"raw_affiliation_string":"School of Arts, Media and Engineering and School of Electrical, Computer and Energy Engineering, Arizona State University,Geometric Media Lab,Tempe,AZ,USA,85281","institution_ids":["https://openalex.org/I55732556"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101704510","display_name":"Hongjun Choi","orcid":"https://orcid.org/0000-0003-4706-934X"},"institutions":[{"id":"https://openalex.org/I55732556","display_name":"Arizona State University","ror":"https://ror.org/03efmqc40","country_code":"US","type":"education","lineage":["https://openalex.org/I55732556"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Hongjun Choi","raw_affiliation_strings":["School of Arts, Media and Engineering and School of Electrical, Computer and Energy Engineering, Arizona State University,Geometric Media Lab,Tempe,AZ,USA,85281"],"affiliations":[{"raw_affiliation_string":"School of Arts, Media and Engineering and School of Electrical, Computer and Energy Engineering, Arizona State University,Geometric Media Lab,Tempe,AZ,USA,85281","institution_ids":["https://openalex.org/I55732556"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5023797933","display_name":"Ankita Shukla","orcid":"https://orcid.org/0000-0002-1878-2667"},"institutions":[{"id":"https://openalex.org/I55732556","display_name":"Arizona State University","ror":"https://ror.org/03efmqc40","country_code":"US","type":"education","lineage":["https://openalex.org/I55732556"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ankita Shukla","raw_affiliation_strings":["School of Arts, Media and Engineering and School of Electrical, Computer and Energy Engineering, Arizona State University,Geometric Media Lab,Tempe,AZ,USA,85281"],"affiliations":[{"raw_affiliation_string":"School of Arts, Media and Engineering and School of Electrical, Computer and Energy Engineering, Arizona State University,Geometric Media Lab,Tempe,AZ,USA,85281","institution_ids":["https://openalex.org/I55732556"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100344808","display_name":"Yuan Wang","orcid":"https://orcid.org/0000-0001-5871-9497"},"institutions":[{"id":"https://openalex.org/I155781252","display_name":"University of South Carolina","ror":"https://ror.org/02b6qw903","country_code":"US","type":"education","lineage":["https://openalex.org/I155781252"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yuan Wang","raw_affiliation_strings":["University of South Carolina,Department of Epidemiology and Biostatistics,Columbia,SC,USA,29208"],"affiliations":[{"raw_affiliation_string":"University of South Carolina,Department of Epidemiology and Biostatistics,Columbia,SC,USA,29208","institution_ids":["https://openalex.org/I155781252"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5000559212","display_name":"Matthew P. Buman","orcid":"https://orcid.org/0000-0002-5130-3162"},"institutions":[{"id":"https://openalex.org/I55732556","display_name":"Arizona State University","ror":"https://ror.org/03efmqc40","country_code":"US","type":"education","lineage":["https://openalex.org/I55732556"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Matthew P. Buman","raw_affiliation_strings":["College of Health Solutions, Arizona State University,Phoenix,AZ,USA,85004"],"affiliations":[{"raw_affiliation_string":"College of Health Solutions, Arizona State University,Phoenix,AZ,USA,85004","institution_ids":["https://openalex.org/I55732556"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5062945520","display_name":"Pavan Turaga","orcid":"https://orcid.org/0000-0002-5263-5943"},"institutions":[{"id":"https://openalex.org/I55732556","display_name":"Arizona State University","ror":"https://ror.org/03efmqc40","country_code":"US","type":"education","lineage":["https://openalex.org/I55732556"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Pavan Turaga","raw_affiliation_strings":["School of Arts, Media and Engineering and School of Electrical, Computer and Energy Engineering, Arizona State University,Geometric Media Lab,Tempe,AZ,USA,85281"],"affiliations":[{"raw_affiliation_string":"School of Arts, Media and Engineering and School of Electrical, Computer and Energy Engineering, Arizona State University,Geometric Media Lab,Tempe,AZ,USA,85281","institution_ids":["https://openalex.org/I55732556"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5003097277"],"corresponding_institution_ids":["https://openalex.org/I55732556"],"apc_list":null,"apc_paid":null,"fwci":0.9052,"has_fulltext":true,"cited_by_count":6,"citation_normalized_percentile":{"value":0.7813409,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":97},"biblio":{"volume":"18","issue":null,"first_page":"837","last_page":"842"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12536","display_name":"Topological and Geometric Data Analysis","score":0.9998999834060669,"subfield":{"id":"https://openalex.org/subfields/1703","display_name":"Computational Theory and Mathematics"},"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/T12536","display_name":"Topological and Geometric Data Analysis","score":0.9998999834060669,"subfield":{"id":"https://openalex.org/subfields/1703","display_name":"Computational Theory and Mathematics"},"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/T12859","display_name":"Cell Image Analysis Techniques","score":0.9549999833106995,"subfield":{"id":"https://openalex.org/subfields/1304","display_name":"Biophysics"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T11266","display_name":"Neuroinflammation and Neurodegeneration Mechanisms","score":0.9301999807357788,"subfield":{"id":"https://openalex.org/subfields/2808","display_name":"Neurology"},"field":{"id":"https://openalex.org/fields/28","display_name":"Neuroscience"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/wearable-computer","display_name":"Wearable computer","score":0.7666906714439392},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6937447786331177},{"id":"https://openalex.org/keywords/topological-data-analysis","display_name":"Topological data analysis","score":0.5079393982887268},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5003430843353271},{"id":"https://openalex.org/keywords/persistent-homology","display_name":"Persistent homology","score":0.4551153779029846},{"id":"https://openalex.org/keywords/wearable-technology","display_name":"Wearable technology","score":0.4479672908782959},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.44762536883354187},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.41925549507141113},{"id":"https://openalex.org/keywords/raw-data","display_name":"Raw data","score":0.4154900312423706},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.34582510590553284},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.215530127286911},{"id":"https://openalex.org/keywords/embedded-system","display_name":"Embedded system","score":0.14081141352653503}],"concepts":[{"id":"https://openalex.org/C150594956","wikidata":"https://www.wikidata.org/wiki/Q1334829","display_name":"Wearable computer","level":2,"score":0.7666906714439392},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6937447786331177},{"id":"https://openalex.org/C2776477805","wikidata":"https://www.wikidata.org/wiki/Q4460773","display_name":"Topological data analysis","level":2,"score":0.5079393982887268},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5003430843353271},{"id":"https://openalex.org/C2874115","wikidata":"https://www.wikidata.org/wiki/Q17099562","display_name":"Persistent homology","level":2,"score":0.4551153779029846},{"id":"https://openalex.org/C54290928","wikidata":"https://www.wikidata.org/wiki/Q4845080","display_name":"Wearable technology","level":3,"score":0.4479672908782959},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.44762536883354187},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.41925549507141113},{"id":"https://openalex.org/C132964779","wikidata":"https://www.wikidata.org/wiki/Q2110223","display_name":"Raw data","level":2,"score":0.4154900312423706},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.34582510590553284},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.215530127286911},{"id":"https://openalex.org/C149635348","wikidata":"https://www.wikidata.org/wiki/Q193040","display_name":"Embedded system","level":1,"score":0.14081141352653503},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1109/ieeeconf56349.2022.10052019","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/ieeeconf56349.2022.10052019","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 56th Asilomar Conference on Signals, Systems, and Computers","raw_type":"proceedings-article"},{"id":"pmid:37583442","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/37583442","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":"Conference record. Asilomar Conference on Signals, Systems & Computers","raw_type":null},{"id":"pmh:oai:pubmedcentral.nih.gov:10426276","is_oa":true,"landing_page_url":"https://www.ncbi.nlm.nih.gov/pmc/articles/10426276","pdf_url":"https://pmc.ncbi.nlm.nih.gov/articles/PMC10426276/pdf/nihms-1920709.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":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Conf Rec Asilomar Conf Signals Syst Comput","raw_type":"Text"}],"best_oa_location":{"id":"pmh:oai:pubmedcentral.nih.gov:10426276","is_oa":true,"landing_page_url":"https://www.ncbi.nlm.nih.gov/pmc/articles/10426276","pdf_url":"https://pmc.ncbi.nlm.nih.gov/articles/PMC10426276/pdf/nihms-1920709.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":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Conf Rec Asilomar Conf Signals Syst Comput","raw_type":"Text"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/4","display_name":"Quality Education","score":0.6000000238418579}],"awards":[{"id":"https://openalex.org/G3611247453","display_name":null,"funder_award_id":"R01GM","funder_id":"https://openalex.org/F4320332161","funder_display_name":"National Institutes of Health"},{"id":"https://openalex.org/G4359693134","display_name":null,"funder_award_id":"NIGMS","funder_id":"https://openalex.org/F4320332161","funder_display_name":"National Institutes of Health"},{"id":"https://openalex.org/G5198503464","display_name":null,"funder_award_id":"R01GM135927","funder_id":"https://openalex.org/F4320332161","funder_display_name":"National Institutes of Health"}],"funders":[{"id":"https://openalex.org/F4320310846","display_name":"University of South Carolina","ror":"https://ror.org/02b6qw903"},{"id":"https://openalex.org/F4320332161","display_name":"National Institutes of Health","ror":"https://ror.org/01cwqze88"},{"id":"https://openalex.org/F4320337354","display_name":"National Institute of General Medical Sciences","ror":"https://ror.org/04q48ey07"},{"id":"https://openalex.org/F4320337380","display_name":"Division of Mathematical Sciences","ror":"https://ror.org/051fftw81"}],"has_content":{"grobid_xml":false,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4323521033.pdf"},"referenced_works_count":33,"referenced_works":["https://openalex.org/W2024060531","https://openalex.org/W2113792310","https://openalex.org/W2294370754","https://openalex.org/W2498184556","https://openalex.org/W2537346363","https://openalex.org/W2795342689","https://openalex.org/W2892765075","https://openalex.org/W2963453233","https://openalex.org/W2963777504","https://openalex.org/W2964137095","https://openalex.org/W2964237352","https://openalex.org/W2971047694","https://openalex.org/W3004127093","https://openalex.org/W3013843370","https://openalex.org/W3033509244","https://openalex.org/W3034368386","https://openalex.org/W3037885005","https://openalex.org/W3045672834","https://openalex.org/W3082444343","https://openalex.org/W3104098829","https://openalex.org/W3110846353","https://openalex.org/W3130702379","https://openalex.org/W3138154797","https://openalex.org/W3152607317","https://openalex.org/W3185170602","https://openalex.org/W3194545001","https://openalex.org/W4200516889","https://openalex.org/W4200634555","https://openalex.org/W4255383393","https://openalex.org/W6638523607","https://openalex.org/W6694154208","https://openalex.org/W6754810493","https://openalex.org/W6798823677"],"related_works":["https://openalex.org/W2886923060","https://openalex.org/W2887690685","https://openalex.org/W4287394697","https://openalex.org/W3119689987","https://openalex.org/W3120766012","https://openalex.org/W3137224016","https://openalex.org/W3128725485","https://openalex.org/W4295883750","https://openalex.org/W3204835693","https://openalex.org/W3106789825"],"abstract_inverted_index":{"Converting":[0],"wearable":[1,36],"sensor":[2,37],"data":[3,38,66,74,87,240],"to":[4,43,128,132,147,253,269],"actionable":[5],"health":[6],"insights":[7],"has":[8,39,68,118,143],"witnessed":[9],"large":[10],"interest":[11],"in":[12,21,29,85,214,226],"recent":[13],"years.":[14],"Deep":[15],"learning":[16,100],"methods":[17,111],"have":[18,23],"been":[19,144],"utilized":[20],"and":[22,45,49,116,181,223],"achieved":[24],"a":[25,59,149,155,166,195,229,260],"lot":[26],"of":[27,62,97,104,246,263],"successes":[28],"various":[30],"applications":[31,123],"involving":[32,124],"wearables":[33,125],"fields.":[34],"However,":[35,102],"unique":[40,261],"issues":[41],"related":[42],"sensitivity":[44],"variability":[46],"between":[47],"subjects,":[48],"dependency":[50],"on":[51,135],"sampling-rate":[52],"for":[53,112],"analysis.":[54],"To":[55,210],"mitigate":[56],"these":[57],"issues,":[58],"different":[60,208,212],"type":[61],"analysis":[63,67,75],"using":[64,154,170,197],"topological":[65,267],"shown":[69],"promise":[70,96],"as":[71,81],"well.":[72],"Topological":[73],"(TDA)":[76],"captures":[77],"robust":[78,230],"features,":[79],"such":[80],"persistence":[82,185,247],"images":[83,186],"(PI),":[84],"complex":[86],"through":[88],"the":[89,95,105,178,188,201,237],"persistent":[90],"homology":[91],"algorithm,":[92],"which":[93,235],"holds":[94],"boosting":[98],"machine":[99],"performance.":[101,256],"because":[103],"computational":[106],"load":[107],"required":[108],"by":[109],"TDA":[110],"large-scale":[113],"data,":[114],"integration":[115],"implementation":[117],"lagged":[119],"behind.":[120],"Further,":[121],"many":[122],"require":[126],"models":[127,173],"be":[129],"compact":[130],"enough":[131],"allow":[133],"deployment":[134],"edge-devices.":[136],"In":[137,161,199],"this":[138,162],"context,":[139],"knowledge":[140],"distillation":[141],"(KD)":[142],"widely":[145],"applied":[146],"generate":[148],"small":[150],"model":[151,232],"(student":[152],"model),":[153],"pre-trained":[156],"high-capacity":[157],"network":[158],"(teacher":[159],"model).":[160],"paper,":[163],"we":[164,218],"propose":[165],"new":[167],"KD":[168],"strategy":[169,222],"two":[171,191],"teacher":[172,251],"-":[174],"one":[175],"that":[176,183,244],"uses":[177,184],"raw":[179],"time-series":[180],"another":[182],"from":[187,204,216],"time-series.":[189],"These":[190],"teachers":[192,206],"then":[193],"train":[194],"student":[196,202,231],"KD.":[198,227],"essence,":[200],"learns":[203],"heterogeneous":[205],"providing":[207],"knowledge.":[209],"consider":[211],"properties":[213],"features":[215,248,268],"teachers,":[217],"apply":[219],"an":[220],"annealing":[221],"adaptive":[224],"temperature":[225],"Finally,":[228],"is":[233],"distilled,":[234],"utilizes":[236],"time":[238],"series":[239],"only.":[241],"We":[242],"find":[243],"incorporation":[245],"via":[249],"second":[250],"leads":[252],"significantly":[254],"improved":[255],"This":[257],"approach":[258],"provides":[259],"way":[262],"fusing":[264],"deep-learning":[265],"with":[266],"develop":[270],"effective":[271],"models.":[272]},"counts_by_year":[{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":1}],"updated_date":"2026-04-21T08:09:41.155169","created_date":"2025-10-10T00:00:00"}
