{"id":"https://openalex.org/W4403582560","doi":"https://doi.org/10.1145/3627673.3679787","title":"Fast Human Action Recognition via Millimeter Wave Radar Point Cloud Sequences Learning","display_name":"Fast Human Action Recognition via Millimeter Wave Radar Point Cloud Sequences Learning","publication_year":2024,"publication_date":"2024-10-20","ids":{"openalex":"https://openalex.org/W4403582560","doi":"https://doi.org/10.1145/3627673.3679787"},"language":"en","primary_location":{"id":"doi:10.1145/3627673.3679787","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3627673.3679787","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 33rd ACM International Conference on Information and Knowledge Management","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/A5043414083","display_name":"Tiancheng Shao","orcid":"https://orcid.org/0009-0009-6549-4949"},"institutions":[{"id":"https://openalex.org/I76569877","display_name":"Southeast University","ror":"https://ror.org/04ct4d772","country_code":"CN","type":"education","lineage":["https://openalex.org/I76569877"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Tongfei Shao","raw_affiliation_strings":["Southeast University, Nanjing, China"],"affiliations":[{"raw_affiliation_string":"Southeast University, Nanjing, China","institution_ids":["https://openalex.org/I76569877"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5109732238","display_name":"Z. Z. Du","orcid":"https://orcid.org/0009-0003-4567-982X"},"institutions":[{"id":"https://openalex.org/I76569877","display_name":"Southeast University","ror":"https://ror.org/04ct4d772","country_code":"CN","type":"education","lineage":["https://openalex.org/I76569877"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zheyu Du","raw_affiliation_strings":["Southeast University, Nanjing, China"],"affiliations":[{"raw_affiliation_string":"Southeast University, Nanjing, China","institution_ids":["https://openalex.org/I76569877"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101979710","display_name":"Chuanyou Li","orcid":"https://orcid.org/0000-0002-8725-6417"},"institutions":[{"id":"https://openalex.org/I76569877","display_name":"Southeast University","ror":"https://ror.org/04ct4d772","country_code":"CN","type":"education","lineage":["https://openalex.org/I76569877"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chuanyou Li","raw_affiliation_strings":["Southeast University, Nanjing, China"],"affiliations":[{"raw_affiliation_string":"Southeast University, Nanjing, China","institution_ids":["https://openalex.org/I76569877"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5059512471","display_name":"Tianxing Wu","orcid":"https://orcid.org/0000-0003-4669-3570"},"institutions":[{"id":"https://openalex.org/I76569877","display_name":"Southeast University","ror":"https://ror.org/04ct4d772","country_code":"CN","type":"education","lineage":["https://openalex.org/I76569877"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Tianxing Wu","raw_affiliation_strings":["Southeast University, Nanjing, China"],"affiliations":[{"raw_affiliation_string":"Southeast University, Nanjing, China","institution_ids":["https://openalex.org/I76569877"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100377137","display_name":"Meng Wang","orcid":"https://orcid.org/0000-0002-2293-1709"},"institutions":[{"id":"https://openalex.org/I116953780","display_name":"Tongji University","ror":"https://ror.org/03rc6as71","country_code":"CN","type":"education","lineage":["https://openalex.org/I116953780"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Meng Wang","raw_affiliation_strings":["Tongji University, Shanghai, China"],"affiliations":[{"raw_affiliation_string":"Tongji University, Shanghai, China","institution_ids":["https://openalex.org/I116953780"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5043414083"],"corresponding_institution_ids":["https://openalex.org/I76569877"],"apc_list":null,"apc_paid":null,"fwci":0.7382,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.68327704,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":96,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"2024","last_page":"2033"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12740","display_name":"Gait Recognition and Analysis","score":0.9941999912261963,"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"}},"topics":[{"id":"https://openalex.org/T12740","display_name":"Gait Recognition and Analysis","score":0.9941999912261963,"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/T10812","display_name":"Human Pose and Action Recognition","score":0.9886999726295471,"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/T12994","display_name":"Infrared Thermography in Medicine","score":0.987500011920929,"subfield":{"id":"https://openalex.org/subfields/2741","display_name":"Radiology, Nuclear Medicine and Imaging"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/radar","display_name":"Radar","score":0.6596456170082092},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6238428950309753},{"id":"https://openalex.org/keywords/extremely-high-frequency","display_name":"Extremely high frequency","score":0.6230003833770752},{"id":"https://openalex.org/keywords/point-cloud","display_name":"Point cloud","score":0.5816806554794312},{"id":"https://openalex.org/keywords/cloud-computing","display_name":"Cloud computing","score":0.5697813630104065},{"id":"https://openalex.org/keywords/action","display_name":"Action (physics)","score":0.4521997272968292},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3912065029144287},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.20754706859588623},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.1826016902923584}],"concepts":[{"id":"https://openalex.org/C554190296","wikidata":"https://www.wikidata.org/wiki/Q47528","display_name":"Radar","level":2,"score":0.6596456170082092},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6238428950309753},{"id":"https://openalex.org/C45764600","wikidata":"https://www.wikidata.org/wiki/Q570342","display_name":"Extremely high frequency","level":2,"score":0.6230003833770752},{"id":"https://openalex.org/C131979681","wikidata":"https://www.wikidata.org/wiki/Q1899648","display_name":"Point cloud","level":2,"score":0.5816806554794312},{"id":"https://openalex.org/C79974875","wikidata":"https://www.wikidata.org/wiki/Q483639","display_name":"Cloud computing","level":2,"score":0.5697813630104065},{"id":"https://openalex.org/C2780791683","wikidata":"https://www.wikidata.org/wiki/Q846785","display_name":"Action (physics)","level":2,"score":0.4521997272968292},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3912065029144287},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.20754706859588623},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.1826016902923584},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3627673.3679787","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3627673.3679787","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 33rd ACM International Conference on Information and Knowledge Management","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":19,"referenced_works":["https://openalex.org/W2131774270","https://openalex.org/W2141336889","https://openalex.org/W2172292165","https://openalex.org/W2211722331","https://openalex.org/W2888486794","https://openalex.org/W2906551905","https://openalex.org/W2979856235","https://openalex.org/W2999905431","https://openalex.org/W3000681136","https://openalex.org/W3014629154","https://openalex.org/W3034266912","https://openalex.org/W3034314779","https://openalex.org/W3034442691","https://openalex.org/W3146968685","https://openalex.org/W3168718178","https://openalex.org/W4210553182","https://openalex.org/W4213419823","https://openalex.org/W4234552385","https://openalex.org/W4381734778"],"related_works":["https://openalex.org/W4244478748","https://openalex.org/W4223488648","https://openalex.org/W2134969820","https://openalex.org/W2251605416","https://openalex.org/W1997222214","https://openalex.org/W2560439919","https://openalex.org/W4389340727","https://openalex.org/W3150465815","https://openalex.org/W2802581102","https://openalex.org/W2369026988"],"abstract_inverted_index":{"Human":[0],"action":[1,55],"recognition":[2,43,56],"using":[3,165,229],"commercial":[4,66],"millimeter":[5,68],"wave":[6,69],"radar":[7,71],"is":[8,117,177],"gaining":[9],"significant":[10],"attention":[11],"in":[12,123,168],"smart":[13,17],"elderly":[14],"care":[15],"and":[16,45,81,160,196,234,282],"homes.":[18],"Due":[19],"to":[20,28,99,119,155,179,221],"privacy":[21],"concerns,":[22],"the":[23,86,101,105,121,124,139,162,184,188,191,206,235],"sensing":[24],"data":[25,79,87],"often":[26],"needs":[27],"be":[29],"processed":[30],"locally":[31],"on":[32,59,257],"embedded":[33,258],"systems":[34],"with":[35,217,260,266],"restricted":[36],"computational":[37,198,262],"resources,":[38],"necessitating":[39],"a":[40,52,92,157,213],"balance":[41],"between":[42],"accuracy":[44,250],"efficiency.":[46],"In":[47,138,205],"this":[48],"paper,":[49],"we":[50,90,132,144,211],"propose":[51],"fast":[53],"human":[54],"framework":[57,74,245,271],"based":[58],"3D":[60,173],"point":[61,107,125,170],"cloud":[62,108,126],"sequences":[63],"generated":[64],"by":[65],"4D":[67],"imaging":[70],"systems.":[72],"The":[73,239],"comprises":[75],"two":[76],"primary":[77],"phases:":[78],"preprocessing":[80,88],"spatial-temporal":[82,129],"feature":[83,130,141,193,203,208,219],"extraction.":[84,204],"During":[85],"phase,":[89],"employ":[91,212],"sliding":[93],"window":[94],"approach":[95],"for":[96,150,200],"frame":[97,174,185],"fusion":[98,175,220],"enhance":[100],"spatial":[102,140,148,181,192],"information":[103],"of":[104,164,190],"sparse":[106,169],"while":[109,276],"retaining":[110,277],"its":[111],"temporal":[112,202,207,223],"features.":[113,224],"Additionally,":[114],"Morton":[115],"coding":[116],"used":[118],"address":[120],"disorderliness":[122],"sequence.":[127],"For":[128],"extraction,":[131],"introduce":[133],"an":[134],"innovative":[135],"two-stage":[136],"algorithm.":[137],"extraction":[142,209],"stage,":[143,210],"initially":[145],"extract":[146,180,222],"local":[147,158],"features":[149,182],"each":[151],"point,":[152],"utilizing":[153],"self-attention":[154],"construct":[156],"graph":[159],"circumvent":[161],"limitations":[163],"Euclidean":[166],"distance":[167],"clouds.":[171],"Subsequently,":[172],"convolution":[176],"applied":[178],"at":[183,286],"level,":[186],"reducing":[187],"length":[189],"map":[194],"sequence":[195],"lowering":[197],"requirements":[199],"subsequent":[201],"modified":[214],"Transformer":[215],"encoder":[216],"fine-grained":[218],"We":[225],"conducted":[226],"comprehensive":[227],"experiments":[228],"both":[230],"our":[231,244,270],"collected":[232],"dataset":[233,237,283],"open":[236],"RadHar.":[238],"experimental":[240],"outcomes":[241],"demonstrate":[242],"that":[243],"not":[246],"only":[247],"improves":[248],"inference":[249,274,279],"but":[251],"also":[252],"maintains":[253],"satisfactory":[254],"real-time":[255],"performance":[256],"platforms":[259],"constrained":[261],"resources.":[263],"When":[264],"compared":[265],"state-of-the-art":[267],"(SOTA)":[268],"methods,":[269],"significantly":[272],"enhances":[273],"speed":[275],"competitive":[278],"accuracy.":[280],"Codes":[281],"are":[284],"available":[285],"https://github.com/Feiyuyu0503/FastHAR.":[287]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":3}],"updated_date":"2026-03-27T05:58:40.876381","created_date":"2025-10-10T00:00:00"}
