{"id":"https://openalex.org/W7147288975","doi":"https://doi.org/10.1109/cnml68938.2026.11452403","title":"Towards a Global Human Detection Scheme Using Full-Lag Autocorrelation and BiGRU","display_name":"Towards a Global Human Detection Scheme Using Full-Lag Autocorrelation and BiGRU","publication_year":2026,"publication_date":"2026-01-30","ids":{"openalex":"https://openalex.org/W7147288975","doi":"https://doi.org/10.1109/cnml68938.2026.11452403"},"language":null,"primary_location":{"id":"doi:10.1109/cnml68938.2026.11452403","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cnml68938.2026.11452403","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2026 International Conference on Communication Networks and Machine Learning (CNML)","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/A5132608547","display_name":"Bing Jin","orcid":null},"institutions":[{"id":"https://openalex.org/I927504317","display_name":"Nanchang Hangkong University","ror":"https://ror.org/0369pvp92","country_code":"CN","type":"education","lineage":["https://openalex.org/I927504317"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Bing Jin","raw_affiliation_strings":["Nanchang Hangkong University,School of Software,Nanchang,China"],"affiliations":[{"raw_affiliation_string":"Nanchang Hangkong University,School of Software,Nanchang,China","institution_ids":["https://openalex.org/I927504317"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5002328317","display_name":"Zhen Wu","orcid":"https://orcid.org/0000-0002-1242-3735"},"institutions":[{"id":"https://openalex.org/I927504317","display_name":"Nanchang Hangkong University","ror":"https://ror.org/0369pvp92","country_code":"CN","type":"education","lineage":["https://openalex.org/I927504317"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhenhua Wu","raw_affiliation_strings":["Nanchang Hangkong University,School of Software,Nanchang,China"],"affiliations":[{"raw_affiliation_string":"Nanchang Hangkong University,School of Software,Nanchang,China","institution_ids":["https://openalex.org/I927504317"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5132608547"],"corresponding_institution_ids":["https://openalex.org/I927504317"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.92085127,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"915","last_page":"918"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10326","display_name":"Indoor and Outdoor Localization Technologies","score":0.9563000202178955,"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":0.9563000202178955,"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/T12740","display_name":"Gait Recognition and Analysis","score":0.004000000189989805,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical 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":0.0034000000450760126,"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/context","display_name":"Context (archaeology)","score":0.6787999868392944},{"id":"https://openalex.org/keywords/autocorrelation","display_name":"Autocorrelation","score":0.6223000288009644},{"id":"https://openalex.org/keywords/scheme","display_name":"Scheme (mathematics)","score":0.5922999978065491},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4880000054836273},{"id":"https://openalex.org/keywords/motion","display_name":"Motion (physics)","score":0.47679999470710754},{"id":"https://openalex.org/keywords/human-motion","display_name":"Human motion","score":0.42489999532699585},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.3926999866962433},{"id":"https://openalex.org/keywords/motion-detection","display_name":"Motion detection","score":0.3799000084400177}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7074999809265137},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7046999931335449},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.6787999868392944},{"id":"https://openalex.org/C5297727","wikidata":"https://www.wikidata.org/wiki/Q786970","display_name":"Autocorrelation","level":2,"score":0.6223000288009644},{"id":"https://openalex.org/C77618280","wikidata":"https://www.wikidata.org/wiki/Q1155772","display_name":"Scheme (mathematics)","level":2,"score":0.5922999978065491},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5618000030517578},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4880000054836273},{"id":"https://openalex.org/C104114177","wikidata":"https://www.wikidata.org/wiki/Q79782","display_name":"Motion (physics)","level":2,"score":0.47679999470710754},{"id":"https://openalex.org/C2986578859","wikidata":"https://www.wikidata.org/wiki/Q657632","display_name":"Human motion","level":3,"score":0.42489999532699585},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.3926999866962433},{"id":"https://openalex.org/C2780624872","wikidata":"https://www.wikidata.org/wiki/Q852453","display_name":"Motion detection","level":3,"score":0.3799000084400177},{"id":"https://openalex.org/C2779843651","wikidata":"https://www.wikidata.org/wiki/Q7390335","display_name":"SIGNAL (programming language)","level":2,"score":0.3774000108242035},{"id":"https://openalex.org/C2780385302","wikidata":"https://www.wikidata.org/wiki/Q367158","display_name":"Protocol (science)","level":3,"score":0.3449999988079071},{"id":"https://openalex.org/C137270730","wikidata":"https://www.wikidata.org/wiki/Q120811","display_name":"Detection theory","level":3,"score":0.31850001215934753},{"id":"https://openalex.org/C2988656282","wikidata":"https://www.wikidata.org/wiki/Q4677630","display_name":"Activity detection","level":2,"score":0.3174000084400177},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2838999927043915},{"id":"https://openalex.org/C104267543","wikidata":"https://www.wikidata.org/wiki/Q208163","display_name":"Signal processing","level":3,"score":0.27950000762939453},{"id":"https://openalex.org/C203595873","wikidata":"https://www.wikidata.org/wiki/Q25389927","display_name":"Change detection","level":2,"score":0.26429998874664307},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2578999996185303},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.25769999623298645},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.25029999017715454}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/cnml68938.2026.11452403","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cnml68938.2026.11452403","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2026 International Conference on Communication Networks and Machine Learning (CNML)","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":15,"referenced_works":["https://openalex.org/W1519309472","https://openalex.org/W1893512822","https://openalex.org/W2005059864","https://openalex.org/W2582846117","https://openalex.org/W2923645141","https://openalex.org/W2943164609","https://openalex.org/W2952065976","https://openalex.org/W2997605245","https://openalex.org/W3128989612","https://openalex.org/W3177241359","https://openalex.org/W4292164070","https://openalex.org/W4306362175","https://openalex.org/W4313427504","https://openalex.org/W4392940098","https://openalex.org/W4403127416"],"related_works":[],"abstract_inverted_index":{"WiFi-based":[0],"human":[1],"detection":[2,96],"often":[3],"fails":[4],"in":[5,66],"unseen":[6,67],"areas":[7,53],"due":[8],"to":[9],"its":[10],"reliance":[11],"on":[12],"location-specific":[13],"signal":[14],"features.":[15],"To":[16],"achieve":[17],"reliable":[18],"cross-region":[19],"generalization,":[20],"we":[21],"propose":[22],"FA-BiGRU.":[23],"It":[24],"first":[25],"extracts":[26],"position-invariant":[27],"motion":[28,81],"patterns":[29],"via":[30],"full-lag":[31],"autocorrelation":[32],"of":[33],"CSI,":[34],"then":[35],"models":[36],"their":[37],"temporal":[38],"context":[39],"with":[40,92],"a":[41,46,93],"Bidirectional":[42],"GRU.":[43],"Evaluated":[44],"under":[45],"strict":[47],"protocol":[48],"where":[49],"training":[50],"and":[51,63],"testing":[52],"are":[54],"strictly":[55],"non-overlapping,":[56],"FA-BiGRU":[57,77],"attains":[58],"99.54%":[59],"accuracy,":[60],"99.59%":[61],"F1-score,":[62],"99.82%":[64],"precision":[65],"sub-regions,":[68],"significantly":[69],"outperforming":[70],"existing":[71],"methods.":[72],"The":[73],"results":[74],"validate":[75],"that":[76],"effectively":[78],"learns":[79],"transferable":[80],"characteristics,":[82],"enabling":[83],"\"train":[84],"once,":[85],"detect":[86],"everywhere\"":[87],"capability":[88],"for":[89],"whole-room":[90],"monitoring":[91],"practical":[94],"1-second":[95],"window.":[97]},"counts_by_year":[],"updated_date":"2026-04-02T13:53:19.096889","created_date":"2026-04-02T00:00:00"}