{"id":"https://openalex.org/W4386025421","doi":"https://doi.org/10.1145/3606193.3606195","title":"Millimeter Wave Radar Fall Detection Algorithm Based on Improved Transformer","display_name":"Millimeter Wave Radar Fall Detection Algorithm Based on Improved Transformer","publication_year":2023,"publication_date":"2023-03-24","ids":{"openalex":"https://openalex.org/W4386025421","doi":"https://doi.org/10.1145/3606193.3606195"},"language":"en","primary_location":{"id":"doi:10.1145/3606193.3606195","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3606193.3606195","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 5th International Symposium on Signal Processing Systems (SSPS)","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/A5027759885","display_name":"Zhiqiang Bao","orcid":"https://orcid.org/0000-0001-9858-6916"},"institutions":[{"id":"https://openalex.org/I4210136859","display_name":"Xi\u2019an University of Posts and Telecommunications","ror":"https://ror.org/04jn0td46","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210136859"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhiqiang Bao","raw_affiliation_strings":["Xi'an University of Posts &amp; Telecommunications, China"],"raw_orcid":"https://orcid.org/0000-0001-9858-6916","affiliations":[{"raw_affiliation_string":"Xi'an University of Posts &amp; Telecommunications, China","institution_ids":["https://openalex.org/I4210136859"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5070191990","display_name":"Ting Ai","orcid":"https://orcid.org/0000-0002-5385-2931"},"institutions":[{"id":"https://openalex.org/I4210136859","display_name":"Xi\u2019an University of Posts and Telecommunications","ror":"https://ror.org/04jn0td46","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210136859"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ting Ai","raw_affiliation_strings":["Xi'an University of Posts &amp; Telecommunications, China"],"raw_orcid":"https://orcid.org/0000-0002-5385-2931","affiliations":[{"raw_affiliation_string":"Xi'an University of Posts &amp; Telecommunications, China","institution_ids":["https://openalex.org/I4210136859"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5000564230","display_name":"Jinhang Su","orcid":"https://orcid.org/0009-0001-4031-1271"},"institutions":[{"id":"https://openalex.org/I4210136859","display_name":"Xi\u2019an University of Posts and Telecommunications","ror":"https://ror.org/04jn0td46","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210136859"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jinhang Su","raw_affiliation_strings":["Xi'an University of Posts &amp; Telecommunications, China"],"raw_orcid":"https://orcid.org/0009-0001-4031-1271","affiliations":[{"raw_affiliation_string":"Xi'an University of Posts &amp; Telecommunications, China","institution_ids":["https://openalex.org/I4210136859"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I4210136859"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.0827199,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"8","last_page":"14"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10326","display_name":"Indoor and Outdoor Localization Technologies","score":0.9958999752998352,"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":0.9958999752998352,"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/T11398","display_name":"Hand Gesture Recognition Systems","score":0.991100013256073,"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"}},{"id":"https://openalex.org/T12740","display_name":"Gait Recognition and Analysis","score":0.9703999757766724,"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/overfitting","display_name":"Overfitting","score":0.7293375730514526},{"id":"https://openalex.org/keywords/extremely-high-frequency","display_name":"Extremely high frequency","score":0.7191463708877563},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6845929622650146},{"id":"https://openalex.org/keywords/transformer","display_name":"Transformer","score":0.6221376657485962},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.592125415802002},{"id":"https://openalex.org/keywords/radar","display_name":"Radar","score":0.56293123960495},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.538034975528717},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5203749537467957},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4341747462749481},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.41813924908638},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3655208349227905},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.3508545756340027},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.17069843411445618},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.16108593344688416},{"id":"https://openalex.org/keywords/electrical-engineering","display_name":"Electrical engineering","score":0.11395013332366943}],"concepts":[{"id":"https://openalex.org/C22019652","wikidata":"https://www.wikidata.org/wiki/Q331309","display_name":"Overfitting","level":3,"score":0.7293375730514526},{"id":"https://openalex.org/C45764600","wikidata":"https://www.wikidata.org/wiki/Q570342","display_name":"Extremely high frequency","level":2,"score":0.7191463708877563},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6845929622650146},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.6221376657485962},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.592125415802002},{"id":"https://openalex.org/C554190296","wikidata":"https://www.wikidata.org/wiki/Q47528","display_name":"Radar","level":2,"score":0.56293123960495},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.538034975528717},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5203749537467957},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4341747462749481},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.41813924908638},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3655208349227905},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.3508545756340027},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.17069843411445618},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.16108593344688416},{"id":"https://openalex.org/C119599485","wikidata":"https://www.wikidata.org/wiki/Q43035","display_name":"Electrical engineering","level":1,"score":0.11395013332366943},{"id":"https://openalex.org/C165801399","wikidata":"https://www.wikidata.org/wiki/Q25428","display_name":"Voltage","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3606193.3606195","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3606193.3606195","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 5th International Symposium on Signal Processing Systems (SSPS)","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":8,"referenced_works":["https://openalex.org/W2183637736","https://openalex.org/W2294653820","https://openalex.org/W2953842422","https://openalex.org/W2979637742","https://openalex.org/W3039610325","https://openalex.org/W3160396525","https://openalex.org/W4298417731","https://openalex.org/W4300949703"],"related_works":["https://openalex.org/W4362597605","https://openalex.org/W1574414179","https://openalex.org/W4297676672","https://openalex.org/W3009056573","https://openalex.org/W2922073769","https://openalex.org/W4281702477","https://openalex.org/W2490526372","https://openalex.org/W4376166922","https://openalex.org/W4378510483","https://openalex.org/W4221142204"],"abstract_inverted_index":{"Aiming":[0],"at":[1],"the":[2,37,42,51,58,63,68,76,81,104,108,114,119],"defects":[3],"of":[4,57,67,78,80,92,107,118],"convolutional":[5],"neural":[6],"network":[7,44,71],"that":[8,103],"it":[9],"is":[10,33,73,87,97,110],"difficult":[11],"to":[12,46],"extract":[13],"high-level":[14],"visual":[15],"semantic":[16],"information":[17,53,56],"and":[18,54,75,116],"ignore":[19],"inter-channel":[20],"information,":[21],"a":[22,48],"millimeter":[23,93],"wave":[24,94],"radar":[25,95],"fall":[26,90],"detection":[27,91],"algorithm":[28,109],"based":[29],"on":[30],"improved":[31],"Transformer":[32,43,82],"proposed.":[34],"By":[35],"combining":[36],"channel":[38],"attention":[39],"mechanism":[40],"with":[41],"structure":[45,83],"form":[47],"pyramid":[49],"structure,":[50],"temporal":[52],"spatial":[55],"signal":[59,96],"are":[60],"effectively":[61],"extracted,":[62],"feature":[64],"extraction":[65],"ability":[66],"deep":[69],"learning":[70],"model":[72],"enhanced,":[74],"problem":[77],"overfitting":[79],"under":[84],"small":[85],"samples":[86],"improved.":[88],"The":[89,99],"realized.":[98],"experimental":[100],"results":[101],"show":[102],"classification":[105],"accuracy":[106],"96.8%,":[111],"which":[112],"verifies":[113],"feasibility":[115],"effectiveness":[117],"model.\\":[120]},"counts_by_year":[],"updated_date":"2026-06-26T08:34:08.712188","created_date":"2025-10-10T00:00:00"}
