{"id":"https://openalex.org/W3109228974","doi":"https://doi.org/10.1145/3384419.3430735","title":"RF-net","display_name":"RF-net","publication_year":2020,"publication_date":"2020-11-16","ids":{"openalex":"https://openalex.org/W3109228974","doi":"https://doi.org/10.1145/3384419.3430735","mag":"3109228974"},"language":"en","primary_location":{"id":"doi:10.1145/3384419.3430735","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3384419.3430735","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 18th Conference on Embedded Networked Sensor Systems","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2111.04566","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Shuya Ding","orcid":null},"institutions":[{"id":"https://openalex.org/I172675005","display_name":"Nanyang Technological University","ror":"https://ror.org/02e7b5302","country_code":"SG","type":"education","lineage":["https://openalex.org/I172675005"]}],"countries":["SG"],"is_corresponding":false,"raw_author_name":"Shuya Ding","raw_affiliation_strings":["Nanyang Technological University, Singapore"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Nanyang Technological University, Singapore","institution_ids":["https://openalex.org/I172675005"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Zhe Chen","orcid":null},"institutions":[{"id":"https://openalex.org/I172675005","display_name":"Nanyang Technological University","ror":"https://ror.org/02e7b5302","country_code":"SG","type":"education","lineage":["https://openalex.org/I172675005"]}],"countries":["SG"],"is_corresponding":false,"raw_author_name":"Zhe Chen","raw_affiliation_strings":["Nanyang Technological University, Singapore"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Nanyang Technological University, Singapore","institution_ids":["https://openalex.org/I172675005"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Tianyue Zheng","orcid":null},"institutions":[{"id":"https://openalex.org/I172675005","display_name":"Nanyang Technological University","ror":"https://ror.org/02e7b5302","country_code":"SG","type":"education","lineage":["https://openalex.org/I172675005"]}],"countries":["SG"],"is_corresponding":false,"raw_author_name":"Tianyue Zheng","raw_affiliation_strings":["Nanyang Technological University, Singapore"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Nanyang Technological University, Singapore","institution_ids":["https://openalex.org/I172675005"]}]},{"author_position":"last","author":{"id":null,"display_name":"Jun Luo","orcid":null},"institutions":[{"id":"https://openalex.org/I172675005","display_name":"Nanyang Technological University","ror":"https://ror.org/02e7b5302","country_code":"SG","type":"education","lineage":["https://openalex.org/I172675005"]}],"countries":["SG"],"is_corresponding":false,"raw_author_name":"Jun Luo","raw_affiliation_strings":["Nanyang Technological University, Singapore"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Nanyang Technological University, Singapore","institution_ids":["https://openalex.org/I172675005"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I172675005"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":157,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"517","last_page":"530"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10326","display_name":"Indoor and Outdoor Localization Technologies","score":0.9998000264167786,"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":0.9998000264167786,"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":0.9713000059127808,"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/T11398","display_name":"Hand Gesture Recognition Systems","score":0.9635000228881836,"subfield":{"id":"https://openalex.org/subfields/1709","display_name":"Human-Computer Interaction"},"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/process","display_name":"Process (computing)","score":0.5737000107765198},{"id":"https://openalex.org/keywords/metric","display_name":"Metric (unit)","score":0.5325000286102295},{"id":"https://openalex.org/keywords/base","display_name":"Base (topology)","score":0.37869998812675476},{"id":"https://openalex.org/keywords/adaptation","display_name":"Adaptation (eye)","score":0.34940001368522644},{"id":"https://openalex.org/keywords/activity-recognition","display_name":"Activity recognition","score":0.33079999685287476},{"id":"https://openalex.org/keywords/radio-frequency","display_name":"Radio frequency","score":0.32899999618530273},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.3140000104904175}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6585999727249146},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.5737000107765198},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.5325000286102295},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.43309998512268066},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.38499999046325684},{"id":"https://openalex.org/C42058472","wikidata":"https://www.wikidata.org/wiki/Q810214","display_name":"Base (topology)","level":2,"score":0.37869998812675476},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.37610000371932983},{"id":"https://openalex.org/C139807058","wikidata":"https://www.wikidata.org/wiki/Q352374","display_name":"Adaptation (eye)","level":2,"score":0.34940001368522644},{"id":"https://openalex.org/C121687571","wikidata":"https://www.wikidata.org/wiki/Q4677630","display_name":"Activity recognition","level":2,"score":0.33079999685287476},{"id":"https://openalex.org/C74064498","wikidata":"https://www.wikidata.org/wiki/Q3396184","display_name":"Radio frequency","level":2,"score":0.32899999618530273},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.3140000104904175},{"id":"https://openalex.org/C133462117","wikidata":"https://www.wikidata.org/wiki/Q4929239","display_name":"Data collection","level":2,"score":0.2849999964237213},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.27489998936653137},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.27140000462532043},{"id":"https://openalex.org/C2776434776","wikidata":"https://www.wikidata.org/wiki/Q19246213","display_name":"Domain adaptation","level":3,"score":0.26809999346733093},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.2587999999523163},{"id":"https://openalex.org/C2776502983","wikidata":"https://www.wikidata.org/wiki/Q690182","display_name":"Contrast (vision)","level":2,"score":0.25780001282691956},{"id":"https://openalex.org/C4554734","wikidata":"https://www.wikidata.org/wiki/Q593744","display_name":"Knowledge base","level":2,"score":0.2572000026702881}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1145/3384419.3430735","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3384419.3430735","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 18th Conference on Embedded Networked Sensor Systems","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2111.04566","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2111.04566","pdf_url":"https://arxiv.org/pdf/2111.04566","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2111.04566","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2111.04566","pdf_url":"https://arxiv.org/pdf/2111.04566","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G7535757919","display_name":null,"funder_award_id":"AcRF Tier2 Grant MOE2016-T2-2-022 and AcRF Tier 1 Grant RG17/19","funder_id":"https://openalex.org/F4320320751","funder_display_name":"Ministry of Education - Singapore"}],"funders":[{"id":"https://openalex.org/F4320320751","display_name":"Ministry of Education - Singapore","ror":"https://ror.org/01kcva023"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":25,"referenced_works":["https://openalex.org/W1985575416","https://openalex.org/W2005666490","https://openalex.org/W2032924581","https://openalex.org/W2104239624","https://openalex.org/W2162002665","https://openalex.org/W2581007094","https://openalex.org/W2611233583","https://openalex.org/W2611843518","https://openalex.org/W2749809371","https://openalex.org/W2754237511","https://openalex.org/W2762698155","https://openalex.org/W2770376512","https://openalex.org/W2827033964","https://openalex.org/W2896542121","https://openalex.org/W2898272312","https://openalex.org/W2898581654","https://openalex.org/W2914439234","https://openalex.org/W2953236873","https://openalex.org/W2964222622","https://openalex.org/W2983307807","https://openalex.org/W3034366997","https://openalex.org/W3107444068","https://openalex.org/W3108816954","https://openalex.org/W4252175165","https://openalex.org/W4252978318"],"related_works":[],"abstract_inverted_index":{"Radio-Frequency":[0],"(RF)":[1],"based":[2,85,182],"device-free":[3,17],"Human":[4],"Activity":[5],"Recognition":[6],"(HAR)":[7],"rises":[8],"as":[9,82],"a":[10,66,83,127,153,174],"promising":[11],"solution":[12],"for":[13,71,95],"many":[14],"applications.":[15],"However,":[16],"(or":[18,30],"contactless)":[19],"sensing":[20,110,187],"is":[21],"often":[22],"more":[23],"sensitive":[24],"to":[25,56,63,73,87,98,121,140,157],"environment":[26,96],"changes":[27],"than":[28],"device-based":[29],"wearable)":[31],"sensing.":[32],"Also,":[33],"RF":[34,109,143,186],"datasets":[35],"strictly":[36],"require":[37],"on-line":[38],"labeling":[39,93],"during":[40],"collection,":[41],"starkly":[42],"different":[43],"from":[44],"image":[45],"and":[46,112,135,147,151],"text":[47],"data":[48,68],"collections":[49],"where":[50,132],"human":[51],"interpretations":[52],"can":[53],"be":[54],"leveraged":[55],"perform":[57],"off-line":[58],"labeling.":[59],"Therefore,":[60],"existing":[61],"solutions":[62],"RF-HAR":[64],"entail":[65],"laborious":[67],"collection":[69],"process":[70],"adapting":[72],"new":[74],"environments.":[75],"To":[76],"this":[77],"end,":[78],"we":[79,104],"propose":[80],"RF-Net":[81,201],"meta-learning":[84,115,155],"approach":[86],"one-shot":[88],"RF-HAR;":[89],"it":[90],"reduces":[91],"the":[92,99,159,164,198],"efforts":[94],"adaptation":[97],"minimum":[100],"level.":[101],"In":[102],"particular,":[103],"first":[105],"examine":[106],"three":[107,185],"representative":[108],"techniques":[111,188],"two":[113,124],"major":[114],"approaches.":[116],"The":[117],"results":[118,195],"motivate":[119],"us":[120],"innovate":[122],"in":[123,189],"designs:":[125],"i)":[126],"dual-path":[128],"base":[129,165],"HAR":[130],"network,":[131,166],"both":[133],"time":[134],"frequency":[136],"domains":[137],"are":[138],"dedicated":[139],"learning":[141],"powerful":[142],"features":[144],"including":[145,167],"spatial":[146],"attention-based":[148],"temporal":[149],"ones,":[150],"ii)":[152],"metric-based":[154],"framework":[156],"enhance":[158],"fast":[160],"adaption":[161],"capability":[162],"of":[163,200],"an":[168],"RF-specific":[169],"metric":[170],"module":[171],"along":[172],"with":[173,203],"residual":[175],"classification":[176],"module.":[177],"We":[178],"conduct":[179],"extensive":[180],"experiments":[181],"on":[183],"all":[184,194],"multiple":[190],"real-world":[191],"indoor":[192],"environments;":[193],"strongly":[196],"demonstrate":[197],"efficacy":[199],"compared":[202],"state-of-the-art":[204],"baselines.":[205]},"counts_by_year":[{"year":2026,"cited_by_count":7},{"year":2025,"cited_by_count":30},{"year":2024,"cited_by_count":37},{"year":2023,"cited_by_count":38},{"year":2022,"cited_by_count":29},{"year":2021,"cited_by_count":14},{"year":2020,"cited_by_count":2}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2020-12-07T00:00:00"}