{"id":"https://openalex.org/W4404294860","doi":"https://doi.org/10.1109/m2vip62491.2024.10746053","title":"Walking Ability Assessment Based on Non-negative Matrix Decomposition and Support Vector Regression","display_name":"Walking Ability Assessment Based on Non-negative Matrix Decomposition and Support Vector Regression","publication_year":2024,"publication_date":"2024-10-03","ids":{"openalex":"https://openalex.org/W4404294860","doi":"https://doi.org/10.1109/m2vip62491.2024.10746053"},"language":"en","primary_location":{"id":"doi:10.1109/m2vip62491.2024.10746053","is_oa":false,"landing_page_url":"https://doi.org/10.1109/m2vip62491.2024.10746053","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 30th International Conference on Mechatronics and Machine Vision in Practice (M2VIP)","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/A5061258700","display_name":"Wenshuang Song","orcid":null},"institutions":[{"id":"https://openalex.org/I196699116","display_name":"Wuhan University of Technology","ror":"https://ror.org/03fe7t173","country_code":"CN","type":"education","lineage":["https://openalex.org/I196699116"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Wenshuang Song","raw_affiliation_strings":["Wuhan University of Technology,School of Information Engineering,Wuhan,China"],"affiliations":[{"raw_affiliation_string":"Wuhan University of Technology,School of Information Engineering,Wuhan,China","institution_ids":["https://openalex.org/I196699116"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5088723765","display_name":"Wei Meng","orcid":"https://orcid.org/0000-0003-0209-8753"},"institutions":[{"id":"https://openalex.org/I196699116","display_name":"Wuhan University of Technology","ror":"https://ror.org/03fe7t173","country_code":"CN","type":"education","lineage":["https://openalex.org/I196699116"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wei Meng","raw_affiliation_strings":["Wuhan University of Technology,School of Information Engineering,Wuhan,China"],"affiliations":[{"raw_affiliation_string":"Wuhan University of Technology,School of Information Engineering,Wuhan,China","institution_ids":["https://openalex.org/I196699116"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102024493","display_name":"Nian Peng","orcid":"https://orcid.org/0009-0001-8836-0398"},"institutions":[{"id":"https://openalex.org/I196699116","display_name":"Wuhan University of Technology","ror":"https://ror.org/03fe7t173","country_code":"CN","type":"education","lineage":["https://openalex.org/I196699116"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Nian Peng","raw_affiliation_strings":["Wuhan University of Technology,School of Information Engineering,Wuhan,China"],"affiliations":[{"raw_affiliation_string":"Wuhan University of Technology,School of Information Engineering,Wuhan,China","institution_ids":["https://openalex.org/I196699116"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5113776738","display_name":"Quan Liu","orcid":null},"institutions":[{"id":"https://openalex.org/I196699116","display_name":"Wuhan University of Technology","ror":"https://ror.org/03fe7t173","country_code":"CN","type":"education","lineage":["https://openalex.org/I196699116"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Quan Liu","raw_affiliation_strings":["Wuhan University of Technology,School of Information Engineering,Wuhan,China"],"affiliations":[{"raw_affiliation_string":"Wuhan University of Technology,School of Information Engineering,Wuhan,China","institution_ids":["https://openalex.org/I196699116"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5061258700"],"corresponding_institution_ids":["https://openalex.org/I196699116"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.1764554,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T14413","display_name":"Advanced Technologies in Various Fields","score":0.7731999754905701,"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"}},"topics":[{"id":"https://openalex.org/T14413","display_name":"Advanced Technologies in Various Fields","score":0.7731999754905701,"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/support-vector-machine","display_name":"Support vector machine","score":0.6825499534606934},{"id":"https://openalex.org/keywords/decomposition","display_name":"Decomposition","score":0.5493124723434448},{"id":"https://openalex.org/keywords/matrix-decomposition","display_name":"Matrix decomposition","score":0.5363273620605469},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.47951963543891907},{"id":"https://openalex.org/keywords/matrix","display_name":"Matrix (chemical analysis)","score":0.46827033162117004},{"id":"https://openalex.org/keywords/regression","display_name":"Regression","score":0.46054384112358093},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.41656577587127686},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.35465872287750244},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3503020107746124},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.34463298320770264},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.2652023434638977},{"id":"https://openalex.org/keywords/materials-science","display_name":"Materials science","score":0.12113615870475769},{"id":"https://openalex.org/keywords/biology","display_name":"Biology","score":0.11900269985198975},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.11777308583259583},{"id":"https://openalex.org/keywords/ecology","display_name":"Ecology","score":0.09600642323493958}],"concepts":[{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.6825499534606934},{"id":"https://openalex.org/C124681953","wikidata":"https://www.wikidata.org/wiki/Q339062","display_name":"Decomposition","level":2,"score":0.5493124723434448},{"id":"https://openalex.org/C42355184","wikidata":"https://www.wikidata.org/wiki/Q1361088","display_name":"Matrix decomposition","level":3,"score":0.5363273620605469},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.47951963543891907},{"id":"https://openalex.org/C106487976","wikidata":"https://www.wikidata.org/wiki/Q685816","display_name":"Matrix (chemical analysis)","level":2,"score":0.46827033162117004},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.46054384112358093},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.41656577587127686},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.35465872287750244},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3503020107746124},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.34463298320770264},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2652023434638977},{"id":"https://openalex.org/C192562407","wikidata":"https://www.wikidata.org/wiki/Q228736","display_name":"Materials science","level":0,"score":0.12113615870475769},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.11900269985198975},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.11777308583259583},{"id":"https://openalex.org/C18903297","wikidata":"https://www.wikidata.org/wiki/Q7150","display_name":"Ecology","level":1,"score":0.09600642323493958},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C159985019","wikidata":"https://www.wikidata.org/wiki/Q181790","display_name":"Composite material","level":1,"score":0.0},{"id":"https://openalex.org/C158693339","wikidata":"https://www.wikidata.org/wiki/Q190524","display_name":"Eigenvalues and eigenvectors","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/m2vip62491.2024.10746053","is_oa":false,"landing_page_url":"https://doi.org/10.1109/m2vip62491.2024.10746053","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 30th International Conference on Mechatronics and Machine Vision in Practice (M2VIP)","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":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":11,"referenced_works":["https://openalex.org/W3708925","https://openalex.org/W1981878435","https://openalex.org/W1991407193","https://openalex.org/W2107555007","https://openalex.org/W2108315391","https://openalex.org/W2517858288","https://openalex.org/W2956621066","https://openalex.org/W3024971933","https://openalex.org/W3027972634","https://openalex.org/W3105175154","https://openalex.org/W4387950732"],"related_works":["https://openalex.org/W2090763504","https://openalex.org/W148178222","https://openalex.org/W2104657898","https://openalex.org/W1948992892","https://openalex.org/W1886884218","https://openalex.org/W1910826599","https://openalex.org/W2012353789","https://openalex.org/W2530420969","https://openalex.org/W2051187167","https://openalex.org/W1980100242"],"abstract_inverted_index":{"Post-stroke":[0],"patients":[1],"often":[2],"experience":[3],"hemiplegia,":[4],"requiring":[5],"timely":[6],"intervention":[7],"for":[8,33,47,113,157],"rehabilitation.":[9],"However,":[10],"an":[11,44],"effective":[12],"rehabilitation":[13,23,35],"program":[14],"can":[15],"only":[16],"be":[17],"developed":[18],"through":[19],"accurate":[20],"assessments.":[21],"Traditional":[22],"scales":[24],"are":[25,92,125],"time-consuming,":[26],"labor-intensive,":[27],"highly":[28],"subjective,":[29],"and":[30,85,104,122,143,147,165],"poorly":[31],"suited":[32],"exoskeleton":[34],"training.":[36],"To":[37],"address":[38],"these":[39],"challenges,":[40],"this":[41],"work":[42],"introduces":[43],"improved":[45],"method":[46,156],"assessing":[48,158],"lower":[49,159],"limb":[50,160],"walking":[51,59,133],"ability":[52],"using":[53,66],"multimodal":[54,166],"synergistic":[55,119],"information":[56],"fusion.":[57],"The":[58],"data":[60,103],"collected":[61],"by":[62],"sensors":[63],"is":[64,79],"decomposed":[65],"non-negative":[67],"matrix":[68],"factorization":[69],"to":[70,82,94,131],"derive":[71],"a":[72,105,111,128,154,163],"collaborative":[73,86],"feature":[74,120],"matrix.":[75],"Singular":[76],"value":[77],"decomposition":[78],"then":[80],"applied":[81],"extract":[83],"eigenvalues":[84],"vectors.":[87],"Subsequently,":[88],"weighted":[89],"similarity":[90,97,123],"metrics":[91],"employed":[93],"measure":[95],"the":[96,118,138,145,150],"between":[98],"each":[99],"set":[100,130],"of":[101,140,149],"motion":[102],"healthy":[106],"control":[107],"template,":[108],"serving":[109],"as":[110,127],"label":[112],"support":[114],"vector":[115],"regression.":[116],"Finally,":[117],"vectors":[121],"measures":[124],"used":[126],"training":[129],"evaluate":[132],"ability.":[134],"This":[135],"approach":[136],"reduces":[137],"impact":[139],"subjective":[141],"assessments":[142],"enhances":[144],"efficiency":[146],"reliability":[148],"evaluation":[151],"process,":[152],"offering":[153],"potential":[155],"mobility":[161],"from":[162],"neuromuscular":[164],"perspective.":[167]},"counts_by_year":[],"updated_date":"2025-12-22T23:10:17.713674","created_date":"2025-10-10T00:00:00"}