{"id":"https://openalex.org/W4384518961","doi":"https://doi.org/10.1109/jsyst.2023.3293482","title":"An Efficient Human Activity Recognition System Using WiFi Channel State Information","display_name":"An Efficient Human Activity Recognition System Using WiFi Channel State Information","publication_year":2023,"publication_date":"2023-07-17","ids":{"openalex":"https://openalex.org/W4384518961","doi":"https://doi.org/10.1109/jsyst.2023.3293482"},"language":"en","primary_location":{"id":"doi:10.1109/jsyst.2023.3293482","is_oa":false,"landing_page_url":"https://doi.org/10.1109/jsyst.2023.3293482","pdf_url":null,"source":{"id":"https://openalex.org/S95999327","display_name":"IEEE Systems Journal","issn_l":"1932-8184","issn":["1932-8184","1937-9234","2373-7816"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["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 Systems Journal","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/A5059022566","display_name":"Wanguo Jiao","orcid":"https://orcid.org/0000-0002-2075-3312"},"institutions":[{"id":"https://openalex.org/I167027274","display_name":"Nanjing Forestry University","ror":"https://ror.org/03m96p165","country_code":"CN","type":"education","lineage":["https://openalex.org/I167027274"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wanguo Jiao","raw_affiliation_strings":["College of Information Science and Technology, Nanjing Forestry University, Nanjing, China"],"raw_orcid":"https://orcid.org/0000-0002-2075-3312","affiliations":[{"raw_affiliation_string":"College of Information Science and Technology, Nanjing Forestry University, Nanjing, China","institution_ids":["https://openalex.org/I167027274"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100687490","display_name":"Changsheng Zhang","orcid":"https://orcid.org/0009-0002-2872-2905"},"institutions":[{"id":"https://openalex.org/I167027274","display_name":"Nanjing Forestry University","ror":"https://ror.org/03m96p165","country_code":"CN","type":"education","lineage":["https://openalex.org/I167027274"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Changsheng Zhang","raw_affiliation_strings":["College of Information Science and Technology, Nanjing Forestry University, Nanjing, China"],"raw_orcid":"https://orcid.org/0009-0002-2872-2905","affiliations":[{"raw_affiliation_string":"College of Information Science and Technology, Nanjing Forestry University, Nanjing, China","institution_ids":["https://openalex.org/I167027274"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I167027274"],"apc_list":null,"apc_paid":null,"fwci":3.5369,"has_fulltext":false,"cited_by_count":42,"citation_normalized_percentile":{"value":0.94068142,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":100},"biblio":{"volume":"17","issue":"4","first_page":"6687","last_page":"6690"},"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/T10860","display_name":"Speech and Audio Processing","score":0.9926000237464905,"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"}},{"id":"https://openalex.org/T11158","display_name":"Wireless Networks and Protocols","score":0.9879000186920166,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7997403144836426},{"id":"https://openalex.org/keywords/gramian-matrix","display_name":"Gramian matrix","score":0.7258902788162231},{"id":"https://openalex.org/keywords/channel-state-information","display_name":"Channel state information","score":0.6708071231842041},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6080089211463928},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5974076390266418},{"id":"https://openalex.org/keywords/field","display_name":"Field (mathematics)","score":0.5365405678749084},{"id":"https://openalex.org/keywords/transformation","display_name":"Transformation (genetics)","score":0.5198436379432678},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.49835824966430664},{"id":"https://openalex.org/keywords/channel","display_name":"Channel (broadcasting)","score":0.45187848806381226},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.44547465443611145},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.4232065975666046},{"id":"https://openalex.org/keywords/wireless","display_name":"Wireless","score":0.25196823477745056},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.08773991465568542},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.0711175799369812}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7997403144836426},{"id":"https://openalex.org/C77246614","wikidata":"https://www.wikidata.org/wiki/Q1409400","display_name":"Gramian matrix","level":3,"score":0.7258902788162231},{"id":"https://openalex.org/C148063708","wikidata":"https://www.wikidata.org/wiki/Q5072511","display_name":"Channel state information","level":3,"score":0.6708071231842041},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6080089211463928},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5974076390266418},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.5365405678749084},{"id":"https://openalex.org/C204241405","wikidata":"https://www.wikidata.org/wiki/Q461499","display_name":"Transformation (genetics)","level":3,"score":0.5198436379432678},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.49835824966430664},{"id":"https://openalex.org/C127162648","wikidata":"https://www.wikidata.org/wiki/Q16858953","display_name":"Channel (broadcasting)","level":2,"score":0.45187848806381226},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.44547465443611145},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4232065975666046},{"id":"https://openalex.org/C555944384","wikidata":"https://www.wikidata.org/wiki/Q249","display_name":"Wireless","level":2,"score":0.25196823477745056},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.08773991465568542},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0711175799369812},{"id":"https://openalex.org/C202444582","wikidata":"https://www.wikidata.org/wiki/Q837863","display_name":"Pure mathematics","level":1,"score":0.0},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","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/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0},{"id":"https://openalex.org/C158693339","wikidata":"https://www.wikidata.org/wiki/Q190524","display_name":"Eigenvalues and eigenvectors","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/jsyst.2023.3293482","is_oa":false,"landing_page_url":"https://doi.org/10.1109/jsyst.2023.3293482","pdf_url":null,"source":{"id":"https://openalex.org/S95999327","display_name":"IEEE Systems Journal","issn_l":"1932-8184","issn":["1932-8184","1937-9234","2373-7816"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["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 Systems Journal","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G2685693439","display_name":null,"funder_award_id":"61701241","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G5958567150","display_name":null,"funder_award_id":"U22B2007","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":15,"referenced_works":["https://openalex.org/W2736051620","https://openalex.org/W2773108469","https://openalex.org/W2884367402","https://openalex.org/W2952065976","https://openalex.org/W2986484781","https://openalex.org/W3044111557","https://openalex.org/W3045907623","https://openalex.org/W3136666733","https://openalex.org/W3196221233","https://openalex.org/W3211515875","https://openalex.org/W4282981352","https://openalex.org/W4388032152","https://openalex.org/W6638667902","https://openalex.org/W6675357634","https://openalex.org/W6839554216"],"related_works":["https://openalex.org/W4390355884","https://openalex.org/W4302344890","https://openalex.org/W3102707439","https://openalex.org/W2436722607","https://openalex.org/W2464112360","https://openalex.org/W3211291180","https://openalex.org/W2498083155","https://openalex.org/W4309006749","https://openalex.org/W2095132598","https://openalex.org/W2063018656"],"abstract_inverted_index":{"Insufficient":[0],"recognition":[1,13,135],"precision":[2],"and":[3,30,59,71,90,101,130,147,172,179],"high":[4],"complexity":[5],"are":[6],"two":[7,51],"main":[8],"challenges":[9],"of":[10,116,143,176],"human":[11],"activity":[12,92],"using":[14],"WiFi":[15],"channel":[16],"state":[17],"information":[18,68],"(CSI),":[19],"which":[20,65,165],"has":[21,153],"attracted":[22],"more":[23],"attention":[24],"due":[25],"to":[26,86],"its":[27],"low":[28,163],"cost":[29],"easy":[31],"realization.":[32],"To":[33],"address":[34],"these":[35,88],"challenges,":[36],"we":[37],"propose":[38],"a":[39,75,79,96],"novel":[40],"framework":[41,49,109,161],"based":[42,137],"on":[43,124,138],"Gramian":[44,54,60],"angular":[45,55,61],"fields":[46],"(GAFs).":[47],"This":[48],"includes":[50],"transformation":[52,97],"methods,":[53],"sum":[56],"field":[57,63],"(GASF)":[58],"difference":[62],"(GADF),":[64],"effectively":[66],"extract":[67],"from":[69],"CSI":[70,105,126],"convert":[72],"it":[73],"into":[74],"CSI-GAF":[76],"image.":[77],"Subsequently,":[78],"convolutional":[80,156],"neural":[81],"network":[82],"(CNN)":[83],"is":[84],"designed":[85],"analyze":[87],"images":[89],"obtain":[91],"information.":[93],"By":[94],"incorporating":[95],"module":[98],"that":[99,133,142],"preserves":[100],"expands":[102],"the":[103,107,111,117,134,139,151,159],"original":[104],"information,":[106],"proposed":[108,160],"utilizes":[110],"powerful":[112],"feature":[113],"extraction":[114],"capabilities":[115],"CNN":[118,152],"in":[119,174],"image":[120],"processing.":[121],"Test":[122],"results":[123],"public":[125],"datasets":[127],"(Wiar,":[128],"SAR,":[129],"Widar3.0)":[131],"demonstrate":[132],"accuracy":[136],"GADF":[140],"outperforms":[141,166],"GASF,":[144],"reaching":[145],"99.4%":[146],"99.0%,":[148],"respectively,":[149],"when":[150],"only":[154],"four":[155],"layers.":[157],"Moreover,":[158],"exhibits":[162],"complexity,":[164],"three":[167],"classical":[168],"models":[169],"(ResNet,":[170],"VGG19,":[171],"ShufflenetV2)":[173],"terms":[175],"both":[177],"parameters":[178],"required":[180],"floating-point":[181],"computations.":[182]},"counts_by_year":[{"year":2026,"cited_by_count":3},{"year":2025,"cited_by_count":18},{"year":2024,"cited_by_count":19},{"year":2023,"cited_by_count":2}],"updated_date":"2026-07-15T18:14:33.161393","created_date":"2025-10-10T00:00:00"}