{"id":"https://openalex.org/W7125897097","doi":"https://doi.org/10.1109/smc58881.2025.11343712","title":"ActionFi: Human Action Recognition via Multimodal Feature Fusion of Restructured CSI and Optical Flow","display_name":"ActionFi: Human Action Recognition via Multimodal Feature Fusion of Restructured CSI and Optical Flow","publication_year":2025,"publication_date":"2025-10-05","ids":{"openalex":"https://openalex.org/W7125897097","doi":"https://doi.org/10.1109/smc58881.2025.11343712"},"language":null,"primary_location":{"id":"doi:10.1109/smc58881.2025.11343712","is_oa":false,"landing_page_url":"https://doi.org/10.1109/smc58881.2025.11343712","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE International Conference on Systems, Man, and Cybernetics (SMC)","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/A5124097232","display_name":"Zheng Zhang","orcid":null},"institutions":[{"id":"https://openalex.org/I2722730","display_name":"Inner Mongolia University","ror":"https://ror.org/0106qb496","country_code":"CN","type":"education","lineage":["https://openalex.org/I2722730"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zheng Zhang","raw_affiliation_strings":["Inner Mongolia University,College of Computer Science,Hohhot,China,010021"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Inner Mongolia University,College of Computer Science,Hohhot,China,010021","institution_ids":["https://openalex.org/I2722730"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5121365585","display_name":"Junxing Zhang","orcid":null},"institutions":[{"id":"https://openalex.org/I2722730","display_name":"Inner Mongolia University","ror":"https://ror.org/0106qb496","country_code":"CN","type":"education","lineage":["https://openalex.org/I2722730"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Junxing Zhang","raw_affiliation_strings":["Inner Mongolia University,College of Computer Science,Hohhot,China,010021"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Inner Mongolia University,College of Computer Science,Hohhot,China,010021","institution_ids":["https://openalex.org/I2722730"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I2722730"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.6557132,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"6824","last_page":"6829"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10326","display_name":"Indoor and Outdoor Localization Technologies","score":0.8496000170707703,"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":0.8496000170707703,"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":0.0430000014603138,"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/T11196","display_name":"Non-Invasive Vital Sign Monitoring","score":0.014000000432133675,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/fuse","display_name":"Fuse (electrical)","score":0.7577000260353088},{"id":"https://openalex.org/keywords/optical-flow","display_name":"Optical flow","score":0.7494999766349792},{"id":"https://openalex.org/keywords/subcarrier","display_name":"Subcarrier","score":0.5885999798774719},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.526199996471405},{"id":"https://openalex.org/keywords/dimension","display_name":"Dimension (graph theory)","score":0.5131999850273132},{"id":"https://openalex.org/keywords/action-recognition","display_name":"Action recognition","score":0.503600001335144},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.49399998784065247},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.4268999993801117}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7742000222206116},{"id":"https://openalex.org/C141353440","wikidata":"https://www.wikidata.org/wiki/Q182221","display_name":"Fuse (electrical)","level":2,"score":0.7577000260353088},{"id":"https://openalex.org/C155542232","wikidata":"https://www.wikidata.org/wiki/Q736111","display_name":"Optical flow","level":3,"score":0.7494999766349792},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6825000047683716},{"id":"https://openalex.org/C198329298","wikidata":"https://www.wikidata.org/wiki/Q586358","display_name":"Subcarrier","level":4,"score":0.5885999798774719},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.526199996471405},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5152999758720398},{"id":"https://openalex.org/C33676613","wikidata":"https://www.wikidata.org/wiki/Q13415176","display_name":"Dimension (graph theory)","level":2,"score":0.5131999850273132},{"id":"https://openalex.org/C2987834672","wikidata":"https://www.wikidata.org/wiki/Q4677630","display_name":"Action recognition","level":3,"score":0.503600001335144},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.49399998784065247},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.4268999993801117},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.41990000009536743},{"id":"https://openalex.org/C2780791683","wikidata":"https://www.wikidata.org/wiki/Q846785","display_name":"Action (physics)","level":2,"score":0.39590001106262207},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.38499999046325684},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.34360000491142273},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.30880001187324524},{"id":"https://openalex.org/C33954974","wikidata":"https://www.wikidata.org/wiki/Q486494","display_name":"Sensor fusion","level":2,"score":0.290800005197525},{"id":"https://openalex.org/C121687571","wikidata":"https://www.wikidata.org/wiki/Q4677630","display_name":"Activity recognition","level":2,"score":0.2897999882698059},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.28790000081062317},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.28380000591278076},{"id":"https://openalex.org/C69744172","wikidata":"https://www.wikidata.org/wiki/Q860822","display_name":"Image fusion","level":3,"score":0.2741999924182892},{"id":"https://openalex.org/C2993807640","wikidata":"https://www.wikidata.org/wiki/Q103709453","display_name":"Attention network","level":2,"score":0.2524999976158142}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/smc58881.2025.11343712","is_oa":false,"landing_page_url":"https://doi.org/10.1109/smc58881.2025.11343712","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE International Conference on Systems, Man, and Cybernetics (SMC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"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":17,"referenced_works":["https://openalex.org/W1977917536","https://openalex.org/W2763219399","https://openalex.org/W3011934803","https://openalex.org/W3116081676","https://openalex.org/W3127460381","https://openalex.org/W3211515875","https://openalex.org/W4205469029","https://openalex.org/W4286544126","https://openalex.org/W4288782290","https://openalex.org/W4295308268","https://openalex.org/W4361801761","https://openalex.org/W4386047772","https://openalex.org/W4386373131","https://openalex.org/W4387079013","https://openalex.org/W4402351976","https://openalex.org/W4402661155","https://openalex.org/W4404964732"],"related_works":[],"abstract_inverted_index":{"WiFi":[0,19],"sensing":[1],"has":[2],"attracted":[3],"increasing":[4],"attention":[5],"from":[6],"researchers":[7],"due":[8],"to":[9,42,90,158,176],"its":[10],"contactless":[11],"and":[12,21,56,65,72,87,121,165,169],"device-free":[13],"characteristics.":[14],"Multimodal":[15],"methods":[16],"based":[17],"on":[18,186],"CSI":[20,55,111,164],"images":[22,126],"have":[23],"made":[24],"significant":[25],"achievements":[26],"in":[27,54,68,131,201],"the":[28,48,51,69,73,84,110,155,160,187,192],"field":[29],"of":[30,50,79,119,128,152,162,174],"human":[31,179],"action":[32,103,142],"recognition.":[33,181],"However,":[34],"there":[35],"are":[36],"also":[37],"some":[38],"issues":[39],"that":[40,195],"need":[41],"be":[43],"addressed,":[44],"such":[45],"as":[46,154],"ignoring":[47],"contribution":[49],"subcarrier":[52,63],"dimension":[53,64],"using":[57],"input":[58],"data":[59,113],"with":[60,115,149],"extremely":[61],"asymmetric":[62],"sample":[66],"size":[67],"recognition":[70,85,104],"process,":[71],"image":[74],"contains":[75],"a":[76,100,146],"large":[77,188],"number":[78,118],"unrelated":[80],"background":[81,137],"factors":[82,138],"about":[83],"subject":[86],"is":[88],"prone":[89],"leaking":[91],"user":[92],"privacy.":[93],"To":[94],"address":[95],"these":[96,171],"issues,":[97],"we":[98],"propose":[99],"novel":[101],"multimodal":[102,132],"method":[105],"called":[106],"ActionFi,":[107],"which":[108],"restructured":[109,163],"into":[112],"blocks":[114],"an":[116],"equal":[117],"subcarriers":[120],"samples.":[122],"Using":[123],"optical":[124,166],"flow":[125,167],"instead":[127],"traditional":[129],"photos":[130],"learning":[133],"can":[134],"eliminate":[135],"irrelevant":[136],"while":[139],"preserving":[140],"essential":[141],"information.":[143],"We":[144,182],"use":[145],"dual-stream":[147],"network":[148],"different":[150],"types":[151],"CNNs":[153],"main":[156],"body":[157],"extract":[159],"features":[161,175],"images,":[168],"fuse":[170],"two":[172],"parts":[173],"achieve":[177],"accurate":[178],"actions":[180],"conducted":[183],"extensive":[184],"experiments":[185],"open-source":[189],"dataset":[190],"MM-Fi,":[191],"results":[193],"show":[194],"our":[196],"ActionFi":[197],"achieved":[198],"state-of-the-art":[199],"performance":[200],"evaluation":[202],"metrics.":[203]},"counts_by_year":[],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2026-01-29T00:00:00"}