{"id":"https://openalex.org/W3121320991","doi":"https://doi.org/10.1109/globecom42002.2020.9322323","title":"Wi-Fi-CSI-based Fall Detection by Spectrogram Analysis with CNN","display_name":"Wi-Fi-CSI-based Fall Detection by Spectrogram Analysis with CNN","publication_year":2020,"publication_date":"2020-12-01","ids":{"openalex":"https://openalex.org/W3121320991","doi":"https://doi.org/10.1109/globecom42002.2020.9322323","mag":"3121320991"},"language":"en","primary_location":{"id":"doi:10.1109/globecom42002.2020.9322323","is_oa":false,"landing_page_url":"https://doi.org/10.1109/globecom42002.2020.9322323","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"GLOBECOM 2020 - 2020 IEEE Global Communications Conference","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/A5101511599","display_name":"Takashi Nakamura","orcid":"https://orcid.org/0000-0002-5201-9868"},"institutions":[{"id":"https://openalex.org/I203951103","display_name":"Keio University","ror":"https://ror.org/02kn6nx58","country_code":"JP","type":"education","lineage":["https://openalex.org/I203951103"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Takashi Nakamura","raw_affiliation_strings":["Graduate School of Science and Technology Keio University, Kanagawa, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Graduate School of Science and Technology Keio University, Kanagawa, Japan","institution_ids":["https://openalex.org/I203951103"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5068994330","display_name":"Mondher Bouazizi","orcid":"https://orcid.org/0000-0001-7055-9318"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Mondher Bouazizi","raw_affiliation_strings":["Kanagawa, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Kanagawa, Japan","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5008604168","display_name":"Kohei Yamamoto","orcid":"https://orcid.org/0000-0001-9669-3566"},"institutions":[{"id":"https://openalex.org/I203951103","display_name":"Keio University","ror":"https://ror.org/02kn6nx58","country_code":"JP","type":"education","lineage":["https://openalex.org/I203951103"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Kohei Yamamoto","raw_affiliation_strings":["Graduate School of Science and Technology Keio University, Kanagawa, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Graduate School of Science and Technology Keio University, Kanagawa, Japan","institution_ids":["https://openalex.org/I203951103"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5016337773","display_name":"Tomoaki Ohtsuki","orcid":"https://orcid.org/0000-0003-3961-1426"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Tomoaki Ohtsuki","raw_affiliation_strings":["Kanagawa, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Kanagawa, Japan","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":9.6613,"has_fulltext":false,"cited_by_count":44,"citation_normalized_percentile":{"value":0.99113527,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"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":0.9998999834060669,"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":0.9998999834060669,"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/T10444","display_name":"Context-Aware Activity Recognition Systems","score":0.9990000128746033,"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/T12222","display_name":"IoT-based Smart Home Systems","score":0.991599977016449,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/spectrogram","display_name":"Spectrogram","score":0.9306822419166565},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8000205755233765},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6966446042060852},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6623969078063965},{"id":"https://openalex.org/keywords/sliding-window-protocol","display_name":"Sliding window protocol","score":0.6110182404518127},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5229713916778564},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.482856810092926},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.4628782272338867},{"id":"https://openalex.org/keywords/channel-state-information","display_name":"Channel state information","score":0.45434850454330444},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.4416694939136505},{"id":"https://openalex.org/keywords/wireless","display_name":"Wireless","score":0.16837656497955322},{"id":"https://openalex.org/keywords/window","display_name":"Window (computing)","score":0.13089555501937866},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.09193503856658936}],"concepts":[{"id":"https://openalex.org/C45273575","wikidata":"https://www.wikidata.org/wiki/Q578970","display_name":"Spectrogram","level":2,"score":0.9306822419166565},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8000205755233765},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6966446042060852},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6623969078063965},{"id":"https://openalex.org/C102392041","wikidata":"https://www.wikidata.org/wiki/Q592860","display_name":"Sliding window protocol","level":3,"score":0.6110182404518127},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5229713916778564},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.482856810092926},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.4628782272338867},{"id":"https://openalex.org/C148063708","wikidata":"https://www.wikidata.org/wiki/Q5072511","display_name":"Channel state information","level":3,"score":0.45434850454330444},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4416694939136505},{"id":"https://openalex.org/C555944384","wikidata":"https://www.wikidata.org/wiki/Q249","display_name":"Wireless","level":2,"score":0.16837656497955322},{"id":"https://openalex.org/C2778751112","wikidata":"https://www.wikidata.org/wiki/Q835016","display_name":"Window (computing)","level":2,"score":0.13089555501937866},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.09193503856658936},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/globecom42002.2020.9322323","is_oa":false,"landing_page_url":"https://doi.org/10.1109/globecom42002.2020.9322323","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"GLOBECOM 2020 - 2020 IEEE Global Communications Conference","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.47999998927116394,"id":"https://metadata.un.org/sdg/16","display_name":"Peace, Justice and strong institutions"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":20,"referenced_works":["https://openalex.org/W2056818943","https://openalex.org/W2072054122","https://openalex.org/W2076068958","https://openalex.org/W2089695767","https://openalex.org/W2095396347","https://openalex.org/W2108598243","https://openalex.org/W2138797044","https://openalex.org/W2194775991","https://openalex.org/W2338892592","https://openalex.org/W2340862004","https://openalex.org/W2514265276","https://openalex.org/W2517331439","https://openalex.org/W2550476060","https://openalex.org/W2765860599","https://openalex.org/W2783857023","https://openalex.org/W2909645133","https://openalex.org/W2964054038","https://openalex.org/W4290728118","https://openalex.org/W6687483927","https://openalex.org/W6729210268"],"related_works":["https://openalex.org/W2530685530","https://openalex.org/W4375868962","https://openalex.org/W2088854863","https://openalex.org/W2011227383","https://openalex.org/W2065606036","https://openalex.org/W1976719989","https://openalex.org/W2942893872","https://openalex.org/W3179495260","https://openalex.org/W4312887852","https://openalex.org/W3127690360"],"abstract_inverted_index":{"Fall":[0],"detection":[1,19,31,107],"system":[2],"has":[3,47],"a":[4,43,103,119,141,190],"great":[5],"demand":[6],"for":[7,57,146,184],"elderly":[8],"people":[9],"living":[10],"alone.":[11],"Wi-Fi":[12,38,109],"CSI":[13,39,115],"(Channel":[14],"State":[15],"Information)":[16],"based":[17,40],"fall":[18,30,41,106,129,155],"method":[20,64,171,197],"can":[21,65,83],"be":[22],"used":[23,56],"to":[24,73,92,163],"build":[25],"non-intrusive":[26],"and":[27,59,81,124,156,186,201],"nonspace-":[28],"limited":[29],"systems.":[32],"In":[33,98],"the":[34,74,78,89,112,126,132,150,154,165,173],"conventional":[35,113,174,199],"work":[36],"on":[37],"detection,":[42],"classification":[44,148,166],"performance":[45,167],"degradation":[46],"been":[48],"observed":[49],"when":[50,88],"data":[51,179],"in":[52,180],"different":[53,182],"environments":[54],"is":[55,96,116],"learning":[58,185],"testing":[60,187],"data.":[61,188],"Also,":[62],"that":[63,194],"not":[66,84],"capture":[67],"accurate":[68],"features":[69],"of":[70,149,153,168],"motion":[71,178],"due":[72],"signal":[75],"distortion":[76],"during":[77],"noise":[79],"reduction,":[80],"it":[82],"segment":[85],"signals":[86],"accurately":[87],"SNR":[90],"(Signal":[91],"Noise":[93],"power":[94],"Ratio)":[95],"small.":[97],"this":[99],"paper,":[100],"we":[101,192],"propose":[102],"spectrogram":[104,133,151],"image-based":[105],"using":[108,131,177],"CSI.":[110,138],"Unlike":[111],"method,":[114],"segmented":[117,137],"with":[118],"certain":[120],"sliding":[121],"time":[122],"window,":[123],"then":[125],"classifier":[127],"detects":[128],"by":[130,176],"image":[134],"generated":[135],"from":[136],"We":[139,159],"use":[140],"CNN":[142],"(Convolutional":[143],"Neural":[144],"Network)":[145],"binary":[147],"images":[152],"non-fall":[157],"motions.":[158],"carried":[160],"out":[161],"experiments":[162],"evaluate":[164],"our":[169,195],"proposed":[170,196],"against":[172],"one":[175,200],"two":[181],"rooms":[183],"As":[189],"result,":[191],"confirmed":[193],"outperformed":[198],"reached":[202],"0.90":[203],"accuracy.":[204]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":9},{"year":2024,"cited_by_count":7},{"year":2023,"cited_by_count":11},{"year":2022,"cited_by_count":10},{"year":2021,"cited_by_count":6}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}
