{"id":"https://openalex.org/W2899023089","doi":"https://doi.org/10.1109/tmc.2018.2878673","title":"A Signal-Level Transfer Learning Framework for Autonomous Reconfiguration of Wearable Systems","display_name":"A Signal-Level Transfer Learning Framework for Autonomous Reconfiguration of Wearable Systems","publication_year":2018,"publication_date":"2018-10-30","ids":{"openalex":"https://openalex.org/W2899023089","doi":"https://doi.org/10.1109/tmc.2018.2878673","mag":"2899023089"},"language":"en","primary_location":{"id":"doi:10.1109/tmc.2018.2878673","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tmc.2018.2878673","pdf_url":null,"source":{"id":"https://openalex.org/S69141925","display_name":"IEEE Transactions on Mobile Computing","issn_l":"1536-1233","issn":["1536-1233","1558-0660","2161-9875"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320439","host_organization_name":"IEEE Computer Society","host_organization_lineage":["https://openalex.org/P4310320439","https://openalex.org/P4310319808"],"host_organization_lineage_names":["IEEE Computer Society","Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Mobile Computing","raw_type":"journal-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/A5073938661","display_name":"Ramyar Saeedi","orcid":"https://orcid.org/0000-0002-5687-3420"},"institutions":[{"id":"https://openalex.org/I72951846","display_name":"Washington State University","ror":"https://ror.org/05dk0ce17","country_code":"US","type":"education","lineage":["https://openalex.org/I72951846"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Ramyar Saeedi","raw_affiliation_strings":["Washington State University, Pullman, USA"],"affiliations":[{"raw_affiliation_string":"Washington State University, Pullman, USA","institution_ids":["https://openalex.org/I72951846"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5049685547","display_name":"Assefaw H. Gebremedhin","orcid":"https://orcid.org/0000-0001-5383-8032"},"institutions":[{"id":"https://openalex.org/I72951846","display_name":"Washington State University","ror":"https://ror.org/05dk0ce17","country_code":"US","type":"education","lineage":["https://openalex.org/I72951846"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Assefaw H. Gebremedhin","raw_affiliation_strings":["Washington State University, Pullman, USA"],"affiliations":[{"raw_affiliation_string":"Washington State University, Pullman, USA","institution_ids":["https://openalex.org/I72951846"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5073938661"],"corresponding_institution_ids":["https://openalex.org/I72951846"],"apc_list":null,"apc_paid":null,"fwci":0.8357,"has_fulltext":false,"cited_by_count":15,"citation_normalized_percentile":{"value":0.79228915,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":"19","issue":"3","first_page":"513","last_page":"527"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10444","display_name":"Context-Aware Activity Recognition Systems","score":0.9994999766349792,"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"}},"topics":[{"id":"https://openalex.org/T10444","display_name":"Context-Aware Activity Recognition Systems","score":0.9994999766349792,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9911999702453613,"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/T10429","display_name":"EEG and Brain-Computer Interfaces","score":0.9818999767303467,"subfield":{"id":"https://openalex.org/subfields/2805","display_name":"Cognitive Neuroscience"},"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/computer-science","display_name":"Computer science","score":0.8717401027679443},{"id":"https://openalex.org/keywords/wearable-computer","display_name":"Wearable computer","score":0.6254213452339172},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.5988804697990417},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5691961050033569},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5558837652206421},{"id":"https://openalex.org/keywords/control-reconfiguration","display_name":"Control reconfiguration","score":0.5558821558952332},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.5108277797698975},{"id":"https://openalex.org/keywords/transfer-of-learning","display_name":"Transfer of learning","score":0.4474034607410431},{"id":"https://openalex.org/keywords/focus","display_name":"Focus (optics)","score":0.41498127579689026},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4141422510147095},{"id":"https://openalex.org/keywords/activity-recognition","display_name":"Activity recognition","score":0.410084992647171}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8717401027679443},{"id":"https://openalex.org/C150594956","wikidata":"https://www.wikidata.org/wiki/Q1334829","display_name":"Wearable computer","level":2,"score":0.6254213452339172},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.5988804697990417},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5691961050033569},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5558837652206421},{"id":"https://openalex.org/C119701452","wikidata":"https://www.wikidata.org/wiki/Q5165881","display_name":"Control reconfiguration","level":2,"score":0.5558821558952332},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.5108277797698975},{"id":"https://openalex.org/C150899416","wikidata":"https://www.wikidata.org/wiki/Q1820378","display_name":"Transfer of learning","level":2,"score":0.4474034607410431},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.41498127579689026},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4141422510147095},{"id":"https://openalex.org/C121687571","wikidata":"https://www.wikidata.org/wiki/Q4677630","display_name":"Activity recognition","level":2,"score":0.410084992647171},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","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/C149635348","wikidata":"https://www.wikidata.org/wiki/Q193040","display_name":"Embedded system","level":1,"score":0.0},{"id":"https://openalex.org/C120665830","wikidata":"https://www.wikidata.org/wiki/Q14620","display_name":"Optics","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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tmc.2018.2878673","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tmc.2018.2878673","pdf_url":null,"source":{"id":"https://openalex.org/S69141925","display_name":"IEEE Transactions on Mobile Computing","issn_l":"1536-1233","issn":["1536-1233","1558-0660","2161-9875"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320439","host_organization_name":"IEEE Computer Society","host_organization_lineage":["https://openalex.org/P4310320439","https://openalex.org/P4310319808"],"host_organization_lineage_names":["IEEE Computer Society","Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Mobile Computing","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G2956472867","display_name":null,"funder_award_id":"IIS-1553528","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":63,"referenced_works":["https://openalex.org/W79168991","https://openalex.org/W229097380","https://openalex.org/W1513731586","https://openalex.org/W1552670949","https://openalex.org/W1595917421","https://openalex.org/W1812533755","https://openalex.org/W1963727881","https://openalex.org/W1988412055","https://openalex.org/W1988790447","https://openalex.org/W2001058334","https://openalex.org/W2006761268","https://openalex.org/W2025055525","https://openalex.org/W2033534238","https://openalex.org/W2037265949","https://openalex.org/W2057907879","https://openalex.org/W2061674567","https://openalex.org/W2087405782","https://openalex.org/W2098043650","https://openalex.org/W2101234009","https://openalex.org/W2108911539","https://openalex.org/W2111179813","https://openalex.org/W2113265222","https://openalex.org/W2120707181","https://openalex.org/W2122838776","https://openalex.org/W2131681506","https://openalex.org/W2136010533","https://openalex.org/W2138159550","https://openalex.org/W2146282931","https://openalex.org/W2157091296","https://openalex.org/W2165698076","https://openalex.org/W2273427657","https://openalex.org/W2296370516","https://openalex.org/W2340025709","https://openalex.org/W2397294223","https://openalex.org/W2407430331","https://openalex.org/W2461428023","https://openalex.org/W2504958336","https://openalex.org/W2534614934","https://openalex.org/W2535317522","https://openalex.org/W2548335893","https://openalex.org/W2580305911","https://openalex.org/W2583183464","https://openalex.org/W2594116048","https://openalex.org/W2608703604","https://openalex.org/W2611126451","https://openalex.org/W2611885129","https://openalex.org/W2620153504","https://openalex.org/W2747415854","https://openalex.org/W2773977228","https://openalex.org/W2782943965","https://openalex.org/W2869166307","https://openalex.org/W2883074505","https://openalex.org/W2888949514","https://openalex.org/W3005347330","https://openalex.org/W3099768174","https://openalex.org/W4247647416","https://openalex.org/W4255421341","https://openalex.org/W4285719527","https://openalex.org/W6608852248","https://openalex.org/W6638441487","https://openalex.org/W6675354045","https://openalex.org/W6677061124","https://openalex.org/W6773842061"],"related_works":["https://openalex.org/W2357657342","https://openalex.org/W2153432761","https://openalex.org/W1580144672","https://openalex.org/W2152623100","https://openalex.org/W2142042635","https://openalex.org/W4248634784","https://openalex.org/W2103296973","https://openalex.org/W3105278570","https://openalex.org/W2117913171","https://openalex.org/W2582769230"],"abstract_inverted_index":{"Machine":[0],"learning":[1,57,158],"algorithms,":[2],"which":[3,32],"form":[4],"the":[5,28,35,49,80,83,88,97,112,116,119,139,153,156,165,168,184,205,219,229],"core":[6],"intelligence":[7],"of":[8,18,24,37,48,72,85,155,167,193],"wearables,":[9],"traditionally":[10],"deduce":[11],"a":[12,16,38,64,198],"computational":[13,39],"model":[14,40],"from":[15],"set":[17],"training":[19,98,113],"data":[20,106,114,117,125,149,169,185],"to":[21,68,92,190,197,215,225],"detect":[22],"events":[23],"interest.":[25],"However,":[26],"in":[27,31,44,96,160],"dynamic":[29],"environment":[30],"wearables":[33],"operate,":[34],"accuracy":[36,212],"drops":[41],"whenever":[42],"changes":[43],"configuration":[45],"or":[46,87],"context":[47],"system":[50],"occur.":[51],"In":[52],"this":[53],"paper,":[54],"using":[55,172],"transfer":[56,157],"as":[58],"an":[59],"organizing":[60],"principle,":[61],"we":[62,77],"propose":[63],"novel":[65],"design":[66],"framework":[67,207],"enable":[69],"autonomous":[70],"reconfiguration":[71],"wearable":[73],"systems.":[74],"More":[75],"specifically,":[76],"focus":[78],"on":[79,144,177],"cases":[81],"where":[82],"specifications":[84],"sensor(s)":[86],"subject":[89],"vary":[90],"compared":[91,196],"what":[93],"is":[94,110,142],"available":[95,175],"data.":[99],"We":[100,163,181,201],"develop":[101],"two":[102,173,191],"new":[103],"algorithms":[104,151,171,187],"for":[105,118,131,218,228],"mapping":[107,109,126,150,170,186],"(the":[108],"between":[111],"and":[115,222],"current":[120],"operating":[121],"setting).":[122],"The":[123,148],"first":[124,220],"algorithm":[127,141],"combines":[128],"effective":[129],"methods":[130],"finding":[132,145],"signal":[133,146],"similarity":[134],"with":[135],"network-based":[136],"clustering,":[137],"while":[138],"second":[140,230],"based":[143],"motifs.":[147],"constitute":[152],"centerpiece":[154],"phase":[159],"our":[161],"framework.":[162],"demonstrate":[164],"efficacy":[166],"publicly":[174],"datasets":[176],"human":[178],"activity":[179,210],"recognition.":[180],"show":[182,203],"that":[183,204],"are":[188],"up":[189,214,224],"orders":[192],"magnitude":[194],"faster":[195],"brute-force":[199],"approach.":[200],"also":[202],"proposed":[206],"overall":[208],"improves":[209],"recognition":[211],"by":[213,223],"15":[216],"percent":[217,227],"dataset":[221],"32":[226],"dataset.":[231]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":3},{"year":2020,"cited_by_count":4},{"year":2019,"cited_by_count":4}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
