{"id":"https://openalex.org/W2783378106","doi":"https://doi.org/10.1109/bigdata.2017.8257960","title":"A closed-loop deep learning architecture for robust activity recognition using wearable sensors","display_name":"A closed-loop deep learning architecture for robust activity recognition using wearable sensors","publication_year":2017,"publication_date":"2017-12-01","ids":{"openalex":"https://openalex.org/W2783378106","doi":"https://doi.org/10.1109/bigdata.2017.8257960","mag":"2783378106"},"language":"en","primary_location":{"id":"doi:10.1109/bigdata.2017.8257960","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata.2017.8257960","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE International Conference on Big Data (Big Data)","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/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":["School of Electrical Engineering and Computer Science, Washington State University, Pullman, WA"],"affiliations":[{"raw_affiliation_string":"School of Electrical Engineering and Computer Science, Washington State University, Pullman, WA","institution_ids":["https://openalex.org/I72951846"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5077340590","display_name":"Skyler Norgaard","orcid":"https://orcid.org/0000-0003-2332-4393"},"institutions":[{"id":"https://openalex.org/I48664048","display_name":"Kalamazoo College","ror":"https://ror.org/001qst305","country_code":"US","type":"education","lineage":["https://openalex.org/I48664048"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Skyler Norgaard","raw_affiliation_strings":["Department of Computer Science, Kalamazoo College"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science, Kalamazoo College","institution_ids":["https://openalex.org/I48664048"]}]},{"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":["School of Electrical Engineering and Computer Science, Washington State University, Pullman, WA"],"affiliations":[{"raw_affiliation_string":"School of Electrical Engineering and Computer Science, Washington State University, Pullman, WA","institution_ids":["https://openalex.org/I72951846"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5073938661"],"corresponding_institution_ids":["https://openalex.org/I72951846"],"apc_list":null,"apc_paid":null,"fwci":1.0012,"has_fulltext":false,"cited_by_count":20,"citation_normalized_percentile":{"value":0.85593204,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"473","last_page":"479"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10444","display_name":"Context-Aware Activity Recognition Systems","score":0.9998000264167786,"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":0.9998000264167786,"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":0.9944000244140625,"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/T12761","display_name":"Data Stream Mining Techniques","score":0.9857000112533569,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/wearable-computer","display_name":"Wearable computer","score":0.7480225563049316},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6794502139091492},{"id":"https://openalex.org/keywords/architecture","display_name":"Architecture","score":0.603676974773407},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.558883786201477},{"id":"https://openalex.org/keywords/closed-loop","display_name":"Closed loop","score":0.5503584146499634},{"id":"https://openalex.org/keywords/wearable-technology","display_name":"Wearable technology","score":0.4752892255783081},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4496779441833496},{"id":"https://openalex.org/keywords/human\u2013computer-interaction","display_name":"Human\u2013computer interaction","score":0.3227041959762573},{"id":"https://openalex.org/keywords/control-engineering","display_name":"Control engineering","score":0.28947174549102783},{"id":"https://openalex.org/keywords/embedded-system","display_name":"Embedded system","score":0.2354450523853302},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.18015044927597046}],"concepts":[{"id":"https://openalex.org/C150594956","wikidata":"https://www.wikidata.org/wiki/Q1334829","display_name":"Wearable computer","level":2,"score":0.7480225563049316},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6794502139091492},{"id":"https://openalex.org/C123657996","wikidata":"https://www.wikidata.org/wiki/Q12271","display_name":"Architecture","level":2,"score":0.603676974773407},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.558883786201477},{"id":"https://openalex.org/C3019251811","wikidata":"https://www.wikidata.org/wiki/Q5135346","display_name":"Closed loop","level":2,"score":0.5503584146499634},{"id":"https://openalex.org/C54290928","wikidata":"https://www.wikidata.org/wiki/Q4845080","display_name":"Wearable technology","level":3,"score":0.4752892255783081},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4496779441833496},{"id":"https://openalex.org/C107457646","wikidata":"https://www.wikidata.org/wiki/Q207434","display_name":"Human\u2013computer interaction","level":1,"score":0.3227041959762573},{"id":"https://openalex.org/C133731056","wikidata":"https://www.wikidata.org/wiki/Q4917288","display_name":"Control engineering","level":1,"score":0.28947174549102783},{"id":"https://openalex.org/C149635348","wikidata":"https://www.wikidata.org/wiki/Q193040","display_name":"Embedded system","level":1,"score":0.2354450523853302},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.18015044927597046},{"id":"https://openalex.org/C153349607","wikidata":"https://www.wikidata.org/wiki/Q36649","display_name":"Visual arts","level":1,"score":0.0},{"id":"https://openalex.org/C142362112","wikidata":"https://www.wikidata.org/wiki/Q735","display_name":"Art","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bigdata.2017.8257960","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata.2017.8257960","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE International Conference on Big Data (Big Data)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"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":30,"referenced_works":["https://openalex.org/W22482183","https://openalex.org/W1510660243","https://openalex.org/W1732926414","https://openalex.org/W1947481528","https://openalex.org/W1963727881","https://openalex.org/W2002261403","https://openalex.org/W2041417904","https://openalex.org/W2057907879","https://openalex.org/W2087405782","https://openalex.org/W2091387935","https://openalex.org/W2102162869","https://openalex.org/W2108911539","https://openalex.org/W2120707181","https://openalex.org/W2126511896","https://openalex.org/W2163605009","https://openalex.org/W2171671120","https://openalex.org/W2270470215","https://openalex.org/W2313023344","https://openalex.org/W2397294223","https://openalex.org/W2473781013","https://openalex.org/W2504958336","https://openalex.org/W2554354235","https://openalex.org/W2583183464","https://openalex.org/W2606107235","https://openalex.org/W2747415854","https://openalex.org/W2903158431","https://openalex.org/W4294382023","https://openalex.org/W6675206559","https://openalex.org/W6684191040","https://openalex.org/W6756615331"],"related_works":["https://openalex.org/W2141434663","https://openalex.org/W2012157391","https://openalex.org/W2585232498","https://openalex.org/W1978333673","https://openalex.org/W2943515292","https://openalex.org/W2562087406","https://openalex.org/W3107039731","https://openalex.org/W1490872123","https://openalex.org/W2983212879","https://openalex.org/W2081594205"],"abstract_inverted_index":{"Human":[0],"activity":[1,41,74,225],"recognition":[2,226],"(HAR)":[3],"plays":[4],"a":[5,34,63,105,112,129,146,203],"central":[6],"role":[7],"in":[8,26,37,95,103,128,163,197],"health-care,":[9],"fitness":[10],"and":[11,46,51,115,157,171],"sport":[12],"applications":[13],"because":[14],"of":[15,28,39,48,72,169,210,216,224,235],"its":[16],"potential":[17],"to":[18,111,165,183,193],"enable":[19],"context-aware":[20],"human":[21,40,73,217],"monitoring.":[22],"With":[23],"the":[24,91,185,190,194,199,208,211,222],"increase":[25],"popularity":[27],"wearable":[29],"devices,":[30],"we":[31,55,135],"are":[32],"witnessing":[33],"large":[35],"influx":[36],"availability":[38],"data.":[42,237],"For":[43],"effective":[44],"analysis":[45],"interpretation":[47],"these":[49],"heterogeneous":[50],"high-volume":[52],"streaming":[53],"data,":[54],"need":[56,65],"powerful":[57],"algorithms.":[58],"In":[59,132],"particular,":[60],"there":[61],"is":[62,109],"strong":[64],"for":[66,69,188],"developing":[67],"algorithms":[68,121],"robust":[70,93],"classification":[71],"data":[75,102,215],"that":[76,107,123,139,221],"specifically":[77],"address":[78],"challenges":[79],"associated":[80],"with":[81,145],"dynamic":[82],"environments":[83],"(e.g.":[84],"different":[85],"users,":[86],"signal":[87],"heterogeneity).":[88],"We":[89,206,219],"use":[90],"term":[92],"here":[94],"two,":[96],"orthogonal":[97],"senses:":[98],"1)":[99],"leveraging":[100],"related":[101],"such":[104,122],"way":[106],"knowledge":[108],"transferred":[110],"new":[113,130,195,204],"context;":[114],"2)":[116],"actively":[117],"reconfiguring":[118],"machine":[119],"learning":[120,143,179],"they":[124],"can":[125],"be":[126],"applied":[127],"context.":[131],"this":[133],"paper,":[134],"propose":[136],"an":[137,141],"architecture":[138,212],"combines":[140],"active":[142,178],"approach":[144],"novel":[147],"deep":[148,151,191],"network.":[149],"Our":[150],"neural":[152],"network":[153,192],"exploits":[154],"both":[155],"Convolutional":[156],"Long":[158],"Short-Term":[159],"Memory":[160],"(LSTM)":[161],"layers":[162],"order":[164],"learn":[166],"hierarchical":[167],"representation":[168],"features":[170],"capture":[172],"time":[173],"dependencies":[174],"from":[175],"raw-data.":[176],"The":[177],"process":[180],"allows":[181],"us":[182],"choose":[184],"best":[186],"instances":[187],"fine-tuning":[189],"setting":[196],"which":[198],"system":[200],"operates":[201],"(i.e.":[202],"subject).":[205],"demonstrate":[207],"efficacy":[209],"using":[213],"real":[214],"activity.":[218],"show":[220],"accuracy":[223],"reaches":[227],"over":[228],"90%":[229],"by":[230],"annotating":[231],"less":[232],"than":[233],"20%":[234],"unlabeled":[236]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2021,"cited_by_count":5},{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":5},{"year":2018,"cited_by_count":5}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}