{"id":"https://openalex.org/W2963998561","doi":"https://doi.org/10.1109/icmew.2017.8026287","title":"Skeleton-based action recognition using LSTM and CNN","display_name":"Skeleton-based action recognition using LSTM and CNN","publication_year":2017,"publication_date":"2017-07-01","ids":{"openalex":"https://openalex.org/W2963998561","doi":"https://doi.org/10.1109/icmew.2017.8026287","mag":"2963998561"},"language":"en","primary_location":{"id":"doi:10.1109/icmew.2017.8026287","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icmew.2017.8026287","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE International Conference on Multimedia &amp; Expo Workshops (ICMEW)","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/A5049344955","display_name":"Chuankun Li","orcid":"https://orcid.org/0000-0001-9427-8780"},"institutions":[{"id":"https://openalex.org/I162868743","display_name":"Tianjin University","ror":"https://ror.org/012tb2g32","country_code":"CN","type":"education","lineage":["https://openalex.org/I162868743"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Chuankun Li","raw_affiliation_strings":["School of Electrical and Information Engineering, Tianjin University, China"],"affiliations":[{"raw_affiliation_string":"School of Electrical and Information Engineering, Tianjin University, China","institution_ids":["https://openalex.org/I162868743"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5042680345","display_name":"Pichao Wang","orcid":"https://orcid.org/0000-0002-1430-0237"},"institutions":[{"id":"https://openalex.org/I204824540","display_name":"University of Wollongong","ror":"https://ror.org/00jtmb277","country_code":"AU","type":"education","lineage":["https://openalex.org/I204824540"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Pichao Wang","raw_affiliation_strings":["Advanced Multimedia Research Lab, University of Wollongong, Australia"],"affiliations":[{"raw_affiliation_string":"Advanced Multimedia Research Lab, University of Wollongong, Australia","institution_ids":["https://openalex.org/I204824540"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100375587","display_name":"Shuang Wang","orcid":"https://orcid.org/0000-0002-6779-9537"},"institutions":[{"id":"https://openalex.org/I162868743","display_name":"Tianjin University","ror":"https://ror.org/012tb2g32","country_code":"CN","type":"education","lineage":["https://openalex.org/I162868743"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shuang Wang","raw_affiliation_strings":["School of Electrical and Information Engineering, Tianjin University, China"],"affiliations":[{"raw_affiliation_string":"School of Electrical and Information Engineering, Tianjin University, China","institution_ids":["https://openalex.org/I162868743"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5103427460","display_name":"Yonghong Hou","orcid":null},"institutions":[{"id":"https://openalex.org/I162868743","display_name":"Tianjin University","ror":"https://ror.org/012tb2g32","country_code":"CN","type":"education","lineage":["https://openalex.org/I162868743"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yonghong Hou","raw_affiliation_strings":["School of Electrical and Information Engineering, Tianjin University, China"],"affiliations":[{"raw_affiliation_string":"School of Electrical and Information Engineering, Tianjin University, China","institution_ids":["https://openalex.org/I162868743"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100695040","display_name":"Wanqing Li","orcid":"https://orcid.org/0000-0002-4427-2687"},"institutions":[{"id":"https://openalex.org/I204824540","display_name":"University of Wollongong","ror":"https://ror.org/00jtmb277","country_code":"AU","type":"education","lineage":["https://openalex.org/I204824540"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Wanqing Li","raw_affiliation_strings":["Advanced Multimedia Research Lab, University of Wollongong, Australia"],"affiliations":[{"raw_affiliation_string":"Advanced Multimedia Research Lab, University of Wollongong, Australia","institution_ids":["https://openalex.org/I204824540"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5049344955"],"corresponding_institution_ids":["https://openalex.org/I162868743"],"apc_list":null,"apc_paid":null,"fwci":5.7344,"has_fulltext":false,"cited_by_count":139,"citation_normalized_percentile":{"value":0.97639182,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":99,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"585","last_page":"590"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10812","display_name":"Human Pose and Action Recognition","score":1.0,"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"}},"topics":[{"id":"https://openalex.org/T10812","display_name":"Human Pose and Action Recognition","score":1.0,"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/T12740","display_name":"Gait Recognition and Analysis","score":0.9944999814033508,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9929999709129333,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/computer-science","display_name":"Computer science","score":0.809476375579834},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7990723848342896},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.7685598134994507},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.686387300491333},{"id":"https://openalex.org/keywords/rgb-color-model","display_name":"RGB color model","score":0.6252962350845337},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.6105339527130127},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5816296339035034},{"id":"https://openalex.org/keywords/action-recognition","display_name":"Action recognition","score":0.4710153341293335},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.46618980169296265},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.4635127782821655},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.4147069454193115},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.35687029361724854},{"id":"https://openalex.org/keywords/class","display_name":"Class (philosophy)","score":0.06599441170692444}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.809476375579834},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7990723848342896},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.7685598134994507},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.686387300491333},{"id":"https://openalex.org/C82990744","wikidata":"https://www.wikidata.org/wiki/Q166194","display_name":"RGB color model","level":2,"score":0.6252962350845337},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.6105339527130127},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5816296339035034},{"id":"https://openalex.org/C2987834672","wikidata":"https://www.wikidata.org/wiki/Q4677630","display_name":"Action recognition","level":3,"score":0.4710153341293335},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.46618980169296265},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.4635127782821655},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.4147069454193115},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.35687029361724854},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.06599441170692444},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0},{"id":"https://openalex.org/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icmew.2017.8026287","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icmew.2017.8026287","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE International Conference on Multimedia &amp; Expo Workshops (ICMEW)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.6000000238418579,"id":"https://metadata.un.org/sdg/4","display_name":"Quality Education"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":31,"referenced_works":["https://openalex.org/W203345490","https://openalex.org/W1950788856","https://openalex.org/W1983592444","https://openalex.org/W2001696967","https://openalex.org/W2007057255","https://openalex.org/W2030061193","https://openalex.org/W2047612595","https://openalex.org/W2048821851","https://openalex.org/W2143267104","https://openalex.org/W2144380653","https://openalex.org/W2172156083","https://openalex.org/W2307035320","https://openalex.org/W2344034899","https://openalex.org/W2510185399","https://openalex.org/W2526041356","https://openalex.org/W2554408731","https://openalex.org/W2591961134","https://openalex.org/W2593146028","https://openalex.org/W2612707971","https://openalex.org/W2761860076","https://openalex.org/W2952587893","https://openalex.org/W2964134613","https://openalex.org/W3098538019","https://openalex.org/W3103858256","https://openalex.org/W6608276133","https://openalex.org/W6640754710","https://openalex.org/W6698200468","https://openalex.org/W6725062358","https://openalex.org/W6734693744","https://openalex.org/W6737954372","https://openalex.org/W6784985713"],"related_works":["https://openalex.org/W4321487865","https://openalex.org/W4313906399","https://openalex.org/W4239306820","https://openalex.org/W4391266461","https://openalex.org/W2590798552","https://openalex.org/W2811106690","https://openalex.org/W2947043951","https://openalex.org/W4399188509","https://openalex.org/W2004108207","https://openalex.org/W4309346246"],"abstract_inverted_index":{"Recent":[0],"methods":[1,36],"based":[2],"on":[3,115],"3D":[4,120,143],"skeleton":[5],"data":[6],"have":[7,37],"achieved":[8,38,112,127],"outstanding":[9],"performance":[10,40],"due":[11],"to":[12,51,64,77],"its":[13],"conciseness,":[14],"robustness,":[15],"and":[16,29,73,96,104,133],"view-independent":[17],"representation.":[18],"With":[19],"the":[20,91,107],"development":[21],"of":[22,131],"deep":[23],"learning,":[24],"Convolutional":[25],"Neural":[26],"Networks":[27],"(CNN)":[28],"Long":[30],"Short":[31],"Term":[32],"Memory":[33],"(LSTM)-based":[34],"learning":[35],"promising":[39],"for":[41,45,106,119],"action":[42,122],"recognition.":[43],"However,":[44],"CNN-based":[46],"methods,":[47],"it":[48],"is":[49,58],"inevitable":[50],"loss":[52],"temporal":[53],"information":[54,69],"when":[55],"a":[56],"sequence":[57],"encoded":[59],"into":[60],"images.":[61],"In":[62,85],"order":[63],"capture":[65],"as":[66,70],"much":[67],"spatial-temporal":[68],"possible,":[71],"LSTM":[72,97,103,105],"CNN":[74,95],"are":[75],"adopted":[76],"conduct":[78],"effective":[79],"recognition":[80],"with":[81],"later":[82],"score":[83,92],"fusion.":[84],"addition,":[86],"experimental":[87],"results":[88,114],"show":[89],"that":[90,101],"fusion":[93],"between":[94,102],"performs":[98],"better":[99],"than":[100],"same":[108],"feature.":[109],"Our":[110],"method":[111,126],"state-of-the-art":[113],"NTU":[116],"RGB+D":[117],"datasets":[118],"human":[121],"analysis.":[123],"The":[124],"proposed":[125],"87.40%":[128],"in":[129,140,148],"terms":[130],"accuracy":[132],"ranked":[134],"1":[135],"<sup":[136],"xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"":[137],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">st</sup>":[138],"place":[139],"Large":[141],"Scale":[142],"Human":[144],"Activity":[145],"Analysis":[146],"Challenge":[147],"Depth":[149],"Videos.":[150]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":8},{"year":2024,"cited_by_count":16},{"year":2023,"cited_by_count":16},{"year":2022,"cited_by_count":17},{"year":2021,"cited_by_count":17},{"year":2020,"cited_by_count":24},{"year":2019,"cited_by_count":24},{"year":2018,"cited_by_count":15}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}