{"id":"https://openalex.org/W2886490782","doi":"https://doi.org/10.1109/icc.2018.8422896","title":"Evaluation and Improvement of Activity Detection Systems with Recurrent Neural Network","display_name":"Evaluation and Improvement of Activity Detection Systems with Recurrent Neural Network","publication_year":2018,"publication_date":"2018-05-01","ids":{"openalex":"https://openalex.org/W2886490782","doi":"https://doi.org/10.1109/icc.2018.8422896","mag":"2886490782"},"language":"en","primary_location":{"id":"doi:10.1109/icc.2018.8422896","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icc.2018.8422896","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 IEEE International Conference on Communications (ICC)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5078386920","display_name":"Chunhai Feng","orcid":"https://orcid.org/0000-0002-9670-922X"},"institutions":[{"id":"https://openalex.org/I189196454","display_name":"The University of Texas at Arlington","ror":"https://ror.org/019kgqr73","country_code":"US","type":"education","lineage":["https://openalex.org/I189196454"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Chunhai Feng","raw_affiliation_strings":["Department of Computer Science and Engineering, The University of Texas at Arlington, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Engineering, The University of Texas at Arlington, USA","institution_ids":["https://openalex.org/I189196454"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5076639377","display_name":"Sheheryar Arshad","orcid":null},"institutions":[{"id":"https://openalex.org/I189196454","display_name":"The University of Texas at Arlington","ror":"https://ror.org/019kgqr73","country_code":"US","type":"education","lineage":["https://openalex.org/I189196454"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Sheheryar Arshad","raw_affiliation_strings":["Department of Computer Science and Engineering, The University of Texas at Arlington, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Engineering, The University of Texas at Arlington, USA","institution_ids":["https://openalex.org/I189196454"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5060088885","display_name":"Ruiyun Yu","orcid":"https://orcid.org/0000-0003-0523-6242"},"institutions":[{"id":"https://openalex.org/I9224756","display_name":"Northeastern University","ror":"https://ror.org/03awzbc87","country_code":"CN","type":"education","lineage":["https://openalex.org/I9224756"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ruiyun Yu","raw_affiliation_strings":["Software College, Northeastern University, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Software College, Northeastern University, China","institution_ids":["https://openalex.org/I9224756"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5001033170","display_name":"Yonghe Liu","orcid":"https://orcid.org/0000-0003-2909-6088"},"institutions":[{"id":"https://openalex.org/I189196454","display_name":"The University of Texas at Arlington","ror":"https://ror.org/019kgqr73","country_code":"US","type":"education","lineage":["https://openalex.org/I189196454"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yonghe Liu","raw_affiliation_strings":["Department of Computer Science and Engineering, The University of Texas at Arlington, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Engineering, The University of Texas at Arlington, USA","institution_ids":["https://openalex.org/I189196454"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":5.8447,"has_fulltext":false,"cited_by_count":24,"citation_normalized_percentile":{"value":0.97446181,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10326","display_name":"Indoor and Outdoor Localization Technologies","score":1.0,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic 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/T10326","display_name":"Indoor and Outdoor Localization Technologies","score":1.0,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic 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/T11158","display_name":"Wireless Networks and Protocols","score":0.9980000257492065,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"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/T10860","display_name":"Speech and Audio Processing","score":0.9976000189781189,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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":0.8386654853820801},{"id":"https://openalex.org/keywords/preprocessor","display_name":"Preprocessor","score":0.6992570161819458},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6446778774261475},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.6424184441566467},{"id":"https://openalex.org/keywords/dynamic-time-warping","display_name":"Dynamic time warping","score":0.5581145882606506},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5102121829986572},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5021677017211914},{"id":"https://openalex.org/keywords/recurrent-neural-network","display_name":"Recurrent neural network","score":0.49481385946273804},{"id":"https://openalex.org/keywords/dimensionality-reduction","display_name":"Dimensionality reduction","score":0.47306904196739197},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.4687514305114746},{"id":"https://openalex.org/keywords/reduction","display_name":"Reduction (mathematics)","score":0.46609604358673096},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4573476016521454},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.45230603218078613},{"id":"https://openalex.org/keywords/identification","display_name":"Identification (biology)","score":0.438640296459198},{"id":"https://openalex.org/keywords/data-pre-processing","display_name":"Data pre-processing","score":0.4195685386657715},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.41006606817245483}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8386654853820801},{"id":"https://openalex.org/C34736171","wikidata":"https://www.wikidata.org/wiki/Q918333","display_name":"Preprocessor","level":2,"score":0.6992570161819458},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6446778774261475},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.6424184441566467},{"id":"https://openalex.org/C88516994","wikidata":"https://www.wikidata.org/wiki/Q1268863","display_name":"Dynamic time warping","level":2,"score":0.5581145882606506},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5102121829986572},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5021677017211914},{"id":"https://openalex.org/C147168706","wikidata":"https://www.wikidata.org/wiki/Q1457734","display_name":"Recurrent neural network","level":3,"score":0.49481385946273804},{"id":"https://openalex.org/C70518039","wikidata":"https://www.wikidata.org/wiki/Q16000077","display_name":"Dimensionality reduction","level":2,"score":0.47306904196739197},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4687514305114746},{"id":"https://openalex.org/C111335779","wikidata":"https://www.wikidata.org/wiki/Q3454686","display_name":"Reduction (mathematics)","level":2,"score":0.46609604358673096},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4573476016521454},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.45230603218078613},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.438640296459198},{"id":"https://openalex.org/C10551718","wikidata":"https://www.wikidata.org/wiki/Q5227332","display_name":"Data pre-processing","level":2,"score":0.4195685386657715},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.41006606817245483},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"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/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C59822182","wikidata":"https://www.wikidata.org/wiki/Q441","display_name":"Botany","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icc.2018.8422896","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icc.2018.8422896","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 IEEE International Conference on Communications (ICC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":14,"referenced_works":["https://openalex.org/W1544232871","https://openalex.org/W1989259731","https://openalex.org/W2002475595","https://openalex.org/W2010882865","https://openalex.org/W2089695767","https://openalex.org/W2095396347","https://openalex.org/W2129149054","https://openalex.org/W2144685889","https://openalex.org/W2172292165","https://openalex.org/W2510977315","https://openalex.org/W2619289925","https://openalex.org/W2735039232","https://openalex.org/W2887884062","https://openalex.org/W2952586546"],"related_works":["https://openalex.org/W3017936921","https://openalex.org/W2989490741","https://openalex.org/W3092506759","https://openalex.org/W2367545121","https://openalex.org/W4248881655","https://openalex.org/W2482165163","https://openalex.org/W3010890513","https://openalex.org/W120741642","https://openalex.org/W138569904","https://openalex.org/W2390914021"],"abstract_inverted_index":{"Channel":[0],"State":[1],"Information":[2],"of":[3,69,103,162],"WiFi":[4],"signal":[5],"has":[6],"attracted":[7],"tremendous":[8],"interests":[9],"in":[10,43,63],"recent":[11],"years":[12],"for":[13,168],"activity":[14,49],"identification.":[15],"Although":[16],"existing":[17],"work":[18],"can":[19,116,131,177],"achieve":[20,178],"desirable":[21],"performance":[22,102],"using":[23],"different":[24,44,57],"algorithms,":[25],"similar":[26],"system":[27,106],"modules":[28,45],"are":[29,61],"often":[30],"shared.":[31],"In":[32,139],"this":[33],"paper,":[34],"we":[35,98,145],"first":[36],"summarize":[37],"and":[38,54,74,82,119,181],"compare":[39,100],"various":[40,71],"techniques":[41],"employed":[42,167],"such":[46],"as":[47],"preprocessing,":[48],"extraction,":[50],"feature":[51,58],"dimension":[52],"reduction,":[53],"classification.":[55,171],"Specifically,":[56],"reduction":[59],"methods":[60],"applied":[62],"order":[64,140],"to":[65,141],"address":[66,142],"the":[67,101],"challenge":[68],"classifying":[70],"length":[72],"signals":[73],"extracting":[75],"representative":[76],"abstractions,":[77],"including":[78],"manually":[79,123],"selecting":[80,124],"features":[81],"Dynamic":[83],"Time":[84],"Warping":[85],"based":[86,129,150],"classification":[87,130],"with":[88,136],"Principal":[89],"Component":[90],"Analysis.":[91],"By":[92],"targeting":[93],"at":[94],"multiple":[95],"human":[96],"activities,":[97],"then":[99],"two":[104],"common":[105],"structures":[107],"from":[108],"difference":[109],"aspects.":[110],"Experimental":[111],"results":[112,173],"show":[113,174],"that":[114,175],"it":[115,176],"be":[117,132],"subjective":[118],"environment":[120],"dependent":[121],"by":[122],"particular":[125],"features,":[126],"while":[127],"DTW":[128],"time":[133],"consuming":[134],"especially":[135],"larger":[137],"dataset.":[138],"these":[143],"challenges,":[144],"propose":[146],"a":[147,160],"novel":[148],"framework":[149],"on":[151],"Deep":[152],"Learning":[153],"Network.":[154],"Long":[155],"Short":[156],"Term":[157],"Memory":[158],"model,":[159],"type":[161],"Recurrent":[163],"Neutral":[164],"Network,":[165],"is":[166],"time-series":[169],"sequence":[170],"Extensive":[172],"higher":[179],"efficiency":[180],"accuracy.":[182]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":4},{"year":2022,"cited_by_count":4},{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":10},{"year":2018,"cited_by_count":2}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}
