{"id":"https://openalex.org/W3198664204","doi":"https://doi.org/10.1109/rcar52367.2021.9517424","title":"Angle Estimation for Lower Limb Joint Movement Based on VMD-NARX Algorithm","display_name":"Angle Estimation for Lower Limb Joint Movement Based on VMD-NARX Algorithm","publication_year":2021,"publication_date":"2021-07-15","ids":{"openalex":"https://openalex.org/W3198664204","doi":"https://doi.org/10.1109/rcar52367.2021.9517424","mag":"3198664204"},"language":"en","primary_location":{"id":"doi:10.1109/rcar52367.2021.9517424","is_oa":false,"landing_page_url":"https://doi.org/10.1109/rcar52367.2021.9517424","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE International Conference on Real-time Computing and Robotics (RCAR)","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/A5111951753","display_name":"Xin Shi","orcid":"https://orcid.org/0000-0003-2477-5217"},"institutions":[{"id":"https://openalex.org/I158842170","display_name":"Chongqing University","ror":"https://ror.org/023rhb549","country_code":"CN","type":"education","lineage":["https://openalex.org/I158842170"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xin Shi","raw_affiliation_strings":["School of Automation, Chongqing University, Chongqing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Automation, Chongqing University, Chongqing, China","institution_ids":["https://openalex.org/I158842170"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5047159198","display_name":"Jieyi Zhang","orcid":"https://orcid.org/0000-0002-6156-0541"},"institutions":[{"id":"https://openalex.org/I158842170","display_name":"Chongqing University","ror":"https://ror.org/023rhb549","country_code":"CN","type":"education","lineage":["https://openalex.org/I158842170"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jieyi Zhang","raw_affiliation_strings":["School of Automation, Chongqing University, Chongqing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Automation, Chongqing University, Chongqing, China","institution_ids":["https://openalex.org/I158842170"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5109600227","display_name":"Pengjie Qin","orcid":null},"institutions":[{"id":"https://openalex.org/I158842170","display_name":"Chongqing University","ror":"https://ror.org/023rhb549","country_code":"CN","type":"education","lineage":["https://openalex.org/I158842170"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Pengjie Qin","raw_affiliation_strings":["School of Automation, Chongqing University, Chongqing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Automation, Chongqing University, Chongqing, China","institution_ids":["https://openalex.org/I158842170"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5078411311","display_name":"Rongyi Liu","orcid":null},"institutions":[{"id":"https://openalex.org/I158842170","display_name":"Chongqing University","ror":"https://ror.org/023rhb549","country_code":"CN","type":"education","lineage":["https://openalex.org/I158842170"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Rongyi Liu","raw_affiliation_strings":["School of Automation, Chongqing University, Chongqing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Automation, Chongqing University, Chongqing, China","institution_ids":["https://openalex.org/I158842170"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I158842170"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"474","last_page":"479"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10784","display_name":"Muscle activation and electromyography studies","score":0.9998000264167786,"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/T10784","display_name":"Muscle activation and electromyography studies","score":0.9998000264167786,"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/T11023","display_name":"Prosthetics and Rehabilitation Robotics","score":0.9879000186920166,"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/T11196","display_name":"Non-Invasive Vital Sign Monitoring","score":0.9740999937057495,"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/artificial-neural-network","display_name":"Artificial neural network","score":0.5100918412208557},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.505075991153717},{"id":"https://openalex.org/keywords/joint","display_name":"Joint (building)","score":0.4848374128341675},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.46567532420158386},{"id":"https://openalex.org/keywords/autoregressive-model","display_name":"Autoregressive model","score":0.4576084315776825},{"id":"https://openalex.org/keywords/nonlinear-autoregressive-exogenous-model","display_name":"Nonlinear autoregressive exogenous model","score":0.41587430238723755},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.41554468870162964},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3419463634490967},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.31862470507621765},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.206377774477005},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.0841321349143982}],"concepts":[{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5100918412208557},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.505075991153717},{"id":"https://openalex.org/C18555067","wikidata":"https://www.wikidata.org/wiki/Q8375051","display_name":"Joint (building)","level":2,"score":0.4848374128341675},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.46567532420158386},{"id":"https://openalex.org/C159877910","wikidata":"https://www.wikidata.org/wiki/Q2202883","display_name":"Autoregressive model","level":2,"score":0.4576084315776825},{"id":"https://openalex.org/C42536954","wikidata":"https://www.wikidata.org/wiki/Q7049462","display_name":"Nonlinear autoregressive exogenous model","level":3,"score":0.41587430238723755},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.41554468870162964},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3419463634490967},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.31862470507621765},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.206377774477005},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.0841321349143982},{"id":"https://openalex.org/C170154142","wikidata":"https://www.wikidata.org/wiki/Q150737","display_name":"Architectural engineering","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/rcar52367.2021.9517424","is_oa":false,"landing_page_url":"https://doi.org/10.1109/rcar52367.2021.9517424","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE International Conference on Real-time Computing and Robotics (RCAR)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.6899999976158142,"display_name":"Life in Land","id":"https://metadata.un.org/sdg/15"}],"awards":[{"id":"https://openalex.org/G2265996673","display_name":null,"funder_award_id":"U1813216","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":15,"referenced_works":["https://openalex.org/W1973586079","https://openalex.org/W1992683873","https://openalex.org/W2000982976","https://openalex.org/W2009026445","https://openalex.org/W2020316512","https://openalex.org/W2024047406","https://openalex.org/W2031190887","https://openalex.org/W2038925006","https://openalex.org/W2072588720","https://openalex.org/W2099019403","https://openalex.org/W2129316790","https://openalex.org/W2150647580","https://openalex.org/W2170034570","https://openalex.org/W2762367260","https://openalex.org/W4238627732"],"related_works":["https://openalex.org/W2606910468","https://openalex.org/W3116827148","https://openalex.org/W4226315710","https://openalex.org/W3120843198","https://openalex.org/W2154965898","https://openalex.org/W2036704594","https://openalex.org/W3083782034","https://openalex.org/W4287185323","https://openalex.org/W2275178414","https://openalex.org/W3123153965"],"abstract_inverted_index":{"Based":[0],"on":[1,34,71,151],"the":[2,10,13,16,21,40,45,64,79,95,104,131,144],"surface":[3],"electromyography":[4],"(sEMG),":[5],"this":[6,25,134,162],"study":[7],"aimed":[8],"at":[9],"control":[11],"of":[12,47,82,90,94,98,107,128,133,146],"fusion":[14],"between":[15],"lower":[17,48,65,83,96,156],"extremity":[18],"exoskeleton":[19],"and":[20,56,103,111,143],"human":[22],"body.":[23],"In":[24],"paper,":[26],"a":[27],"variational":[28,60,152],"mode":[29,61,153],"decomposition":[30,154],"(vmd)":[31],"algorithm":[32],"based":[33,70,150],"neural":[35,73],"network":[36,74],"is":[37,75,136,155,164],"proposed.":[38],"Firstly,":[39],"sEMG":[41],"signals":[42,89],"collected":[43],"in":[44],"process":[46],"limb":[49,66,84],"movement":[50],"are":[51],"denoised":[52,58],"by":[53,59],"band-pass":[54],"filter,":[55],"then":[57],"decomposition.":[62],"Finally,":[63],"angle":[67,171],"prediction":[68,81,148,172],"model":[69,149],"NARX":[72,147],"established":[76],"to":[77],"realize":[78],"continuous":[80],"joint":[85,105],"angle.":[86],"The":[87,116],"electromyographic":[88],"10":[91],"muscle":[92],"groups":[93],"limbs":[97],"5":[99],"subjects":[100],"were":[101,114],"collected,":[102],"angles":[106],"walking,":[108],"sitting":[109],"up":[110],"crossing":[112],"obstacles":[113],"estimated.":[115],"experimental":[117],"results":[118],"show":[119],"that":[120,161],"VMD":[121],"takes":[122],"102":[123],"ms":[124],"for":[125,168],"feature":[126],"extraction":[127],"600":[129],"points,":[130],"speed":[132],"method":[135,163,167],"higher":[137],"than":[138,157],"LMS":[139],"random":[140],"forest":[141],"(LMS-RF),":[142],"RMSE":[145],"2,":[158],"which":[159],"proves":[160],"an":[165],"effective":[166],"establishing":[169],"high-precision":[170],"model.":[173]},"counts_by_year":[{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}