{"id":"https://openalex.org/W4392248612","doi":"https://doi.org/10.1109/icce59016.2024.10444156","title":"An Examination of Learning Data for Behavior Identification and Range Expansion Using Doppler Sensor","display_name":"An Examination of Learning Data for Behavior Identification and Range Expansion Using Doppler Sensor","publication_year":2024,"publication_date":"2024-01-06","ids":{"openalex":"https://openalex.org/W4392248612","doi":"https://doi.org/10.1109/icce59016.2024.10444156"},"language":"en","primary_location":{"id":"doi:10.1109/icce59016.2024.10444156","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icce59016.2024.10444156","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE International Conference on Consumer Electronics (ICCE)","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/A5094023345","display_name":"Kota Sonohara","orcid":null},"institutions":[{"id":"https://openalex.org/I148798404","display_name":"Tokyo University of Technology","ror":"https://ror.org/021a26605","country_code":"JP","type":"education","lineage":["https://openalex.org/I148798404"]}],"countries":["JP"],"is_corresponding":true,"raw_author_name":"Kota Sonohara","raw_affiliation_strings":["Tokyo University of Technology Graduate School of Bionics,Computer and Media Sciences, Hachioji,Tokyo,Japan,192-0982"],"affiliations":[{"raw_affiliation_string":"Tokyo University of Technology Graduate School of Bionics,Computer and Media Sciences, Hachioji,Tokyo,Japan,192-0982","institution_ids":["https://openalex.org/I148798404"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5094023346","display_name":"Hiroshi Tubokawa","orcid":null},"institutions":[{"id":"https://openalex.org/I148798404","display_name":"Tokyo University of Technology","ror":"https://ror.org/021a26605","country_code":"JP","type":"education","lineage":["https://openalex.org/I148798404"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Hiroshi Tubokawa","raw_affiliation_strings":["Tokyo University of Technology Graduate School of Bionics,Computer and Media Sciences, Hachioji,Tokyo,Japan,192-0982"],"affiliations":[{"raw_affiliation_string":"Tokyo University of Technology Graduate School of Bionics,Computer and Media Sciences, Hachioji,Tokyo,Japan,192-0982","institution_ids":["https://openalex.org/I148798404"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5094023345"],"corresponding_institution_ids":["https://openalex.org/I148798404"],"apc_list":null,"apc_paid":null,"fwci":0.2307,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.47062139,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"5"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10326","display_name":"Indoor and Outdoor Localization Technologies","score":0.9927999973297119,"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":0.9927999973297119,"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/T12222","display_name":"IoT-based Smart Home Systems","score":0.9836999773979187,"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/T10080","display_name":"Energy Efficient Wireless Sensor Networks","score":0.9812999963760376,"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/identification","display_name":"Identification (biology)","score":0.5626285076141357},{"id":"https://openalex.org/keywords/doppler-effect","display_name":"Doppler effect","score":0.5345299243927002},{"id":"https://openalex.org/keywords/range","display_name":"Range (aeronautics)","score":0.5234167575836182},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.49258261919021606},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3896225094795227},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.17543208599090576},{"id":"https://openalex.org/keywords/aerospace-engineering","display_name":"Aerospace engineering","score":0.07186609506607056},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.06636938452720642}],"concepts":[{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.5626285076141357},{"id":"https://openalex.org/C142757262","wikidata":"https://www.wikidata.org/wiki/Q76436","display_name":"Doppler effect","level":2,"score":0.5345299243927002},{"id":"https://openalex.org/C204323151","wikidata":"https://www.wikidata.org/wiki/Q905424","display_name":"Range (aeronautics)","level":2,"score":0.5234167575836182},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.49258261919021606},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3896225094795227},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.17543208599090576},{"id":"https://openalex.org/C146978453","wikidata":"https://www.wikidata.org/wiki/Q3798668","display_name":"Aerospace engineering","level":1,"score":0.07186609506607056},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.06636938452720642},{"id":"https://openalex.org/C1276947","wikidata":"https://www.wikidata.org/wiki/Q333","display_name":"Astronomy","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},{"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/icce59016.2024.10444156","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icce59016.2024.10444156","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE International Conference on Consumer Electronics (ICCE)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":10,"referenced_works":["https://openalex.org/W1605510164","https://openalex.org/W1989665047","https://openalex.org/W2028656089","https://openalex.org/W2117811484","https://openalex.org/W2538294022","https://openalex.org/W2626065224","https://openalex.org/W2746870488","https://openalex.org/W2790943595","https://openalex.org/W2971424155","https://openalex.org/W3115542882"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2899084033","https://openalex.org/W2748952813","https://openalex.org/W2390279801","https://openalex.org/W4391913857","https://openalex.org/W2358668433","https://openalex.org/W4396701345","https://openalex.org/W2376932109","https://openalex.org/W2001405890","https://openalex.org/W4396696052"],"abstract_inverted_index":{"In":[0,16,29,67,97,135,198],"recent":[1],"years,":[2],"the":[3,12,30,54,65,119,123,140,145,152,156,161,169,176,183,204,218,223,241,247],"rate":[4],"of":[5,14,32,48,69,92,155,164,206,255,269],"older":[6],"people":[7,22,94,271],"has":[8,59],"been":[9,60],"increasing":[10],"with":[11,252],"aging":[13],"population.":[15],"such":[17,83,190],"a":[18,46,127,165],"situation,":[19],"many":[20],"elderly":[21,93,270],"suffer":[23],"injuries":[24],"from":[25,122,226],"falls":[26,85,106],"every":[27],"year.":[28],"case":[31],"living":[33,95,146,272],"alone,":[34,273],"it":[35,75,200],"is":[36,45,64,76,260],"difficult":[37],"to":[38,53,78,104,150,262],"find":[39],"accidents":[40],"by":[41,133,212,221],"others,":[42],"and":[43,86,88,125,148,159,168,193,208,233,236,244,274],"there":[44],"possibility":[47],"sequelae":[49],"or":[50,181],"death":[51],"due":[52],"delay":[55],"in":[56,107,144,240,246],"discovery.":[57],"It":[58],"reported":[61],"that":[62,74,129,203],"this":[63,70,98,108],"case.":[66],"view":[68],"background,":[71],"we":[72,100,137],"think":[73],"necessary":[77],"quickly":[79],"identify":[80],"dangerous":[81,188,267],"situations":[82,91,189,268],"as":[84,191],"stumbling":[87],"daily":[89],"life":[90],"alone.":[96],"paper,":[99],"use":[101],"Doppler":[102],"sensors":[103],"detect":[105],"study.":[109],"We":[110],"perform":[111],"spectral":[112],"analysis":[113],"using":[114],"short-time":[115],"Fourier":[116],"transform":[117],"on":[118,139,175,182,217],"signals":[120],"obtained":[121],"sensors,":[124],"construct":[126],"classifier":[128],"identifies":[130],"falling":[131],"actions":[132],"CNN.":[134],"addition,":[136,199],"focus":[138],"overall":[141],"activity":[142],"recognition":[143],"space,":[147],"aim":[149],"expand":[151],"practical":[153],"range":[154,163],"proposed":[157],"system,":[158],"examine":[160],"measurement":[162],"single":[166],"sensor":[167],"learning":[170,222],"data.":[171],"Even":[172],"when":[173,215],"installed":[174,216],"wall":[177],"(for":[178,185],"horizontal":[179,242],"detection)":[180],"ceiling":[184],"vertical":[186,248],"detection),":[187],"\u201cFall\u201d":[192,235],"\u201dStumble\u201d":[194,237],"were":[195,250],"100%":[196],"identifiable.":[197],"was":[201,210],"revealed":[202],"identification":[205],"\u201dDown\u201d":[207],"\u201dUp\u201d":[209],"improved":[211],"about":[213],"13.3%":[214],"ceiling.":[219],"Furthermore,":[220],"motion":[224],"data":[225],"directly":[227],"below":[228],"at":[229],"1m,":[230],"2m,":[231],"3m,":[232],"4m,":[234],"within":[238],"\u00b14m":[239],"direction":[243,249],"\u00b12m":[245],"identifiable":[251],"an":[253],"accuracy":[254],"more":[256],"than":[257],"83.3%.This":[258],"system":[259],"considered":[261],"be":[263],"useful":[264],"for":[265],"detecting":[266],"may":[275],"improve":[276],"their":[277],"safety.":[278]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
