{"id":"https://openalex.org/W2755434884","doi":"https://doi.org/10.1109/embc.2017.8037193","title":"Exploring optimal myoelectric feature indices for forearm control strategy using robust principal component analysis","display_name":"Exploring optimal myoelectric feature indices for forearm control strategy using robust principal component analysis","publication_year":2017,"publication_date":"2017-07-01","ids":{"openalex":"https://openalex.org/W2755434884","doi":"https://doi.org/10.1109/embc.2017.8037193","mag":"2755434884","pmid":"https://pubmed.ncbi.nlm.nih.gov/29060237"},"language":"en","primary_location":{"id":"doi:10.1109/embc.2017.8037193","is_oa":false,"landing_page_url":"https://doi.org/10.1109/embc.2017.8037193","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref","pubmed"],"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/A5025266672","display_name":"Suguru Kanoga","orcid":"https://orcid.org/0000-0002-2214-3622"},"institutions":[{"id":"https://openalex.org/I73613424","display_name":"National Institute of Advanced Industrial Science and Technology","ror":"https://ror.org/01703db54","country_code":"JP","type":"government","lineage":["https://openalex.org/I73613424"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Suguru Kanoga","raw_affiliation_strings":["Artificial Intelligence Research Center, National Institute of Advanced Industrial Science and Technology (AIST), Tokyo, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Artificial Intelligence Research Center, National Institute of Advanced Industrial Science and Technology (AIST), Tokyo, Japan","institution_ids":["https://openalex.org/I73613424"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5059580603","display_name":"Akihiko Murai","orcid":"https://orcid.org/0000-0002-2035-4346"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Akihiko Murai","raw_affiliation_strings":["Digital Human Research Group, AIST, Tokyo, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Digital Human Research Group, AIST, Tokyo, Japan","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5011350023","display_name":"Mitsunori Tada","orcid":"https://orcid.org/0000-0001-6784-2016"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Mitsunori Tada","raw_affiliation_strings":["Digital Human Research Group, AIST, Tokyo, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Digital Human Research Group, AIST, Tokyo, Japan","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.5047,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.60055539,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":"2017","issue":null,"first_page":"1796","last_page":"1799"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10784","display_name":"Muscle activation and electromyography studies","score":1.0,"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":1.0,"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/T10338","display_name":"Advanced Sensor and Energy Harvesting Materials","score":0.9980999827384949,"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/T11601","display_name":"Neuroscience and Neural Engineering","score":0.9936000108718872,"subfield":{"id":"https://openalex.org/subfields/2804","display_name":"Cellular and Molecular Neuroscience"},"field":{"id":"https://openalex.org/fields/28","display_name":"Neuroscience"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/principal-component-analysis","display_name":"Principal component analysis","score":0.8611763715744019},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.7801468372344971},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6286451816558838},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5877684354782104},{"id":"https://openalex.org/keywords/histogram","display_name":"Histogram","score":0.5731521248817444},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5460221767425537},{"id":"https://openalex.org/keywords/feature-vector","display_name":"Feature vector","score":0.5189549922943115},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.4723585546016693},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.4597262740135193},{"id":"https://openalex.org/keywords/autoregressive-model","display_name":"Autoregressive model","score":0.41928496956825256},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.2985433042049408},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.16785591840744019}],"concepts":[{"id":"https://openalex.org/C27438332","wikidata":"https://www.wikidata.org/wiki/Q2873","display_name":"Principal component analysis","level":2,"score":0.8611763715744019},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.7801468372344971},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6286451816558838},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5877684354782104},{"id":"https://openalex.org/C53533937","wikidata":"https://www.wikidata.org/wiki/Q185020","display_name":"Histogram","level":3,"score":0.5731521248817444},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5460221767425537},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.5189549922943115},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.4723585546016693},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.4597262740135193},{"id":"https://openalex.org/C159877910","wikidata":"https://www.wikidata.org/wiki/Q2202883","display_name":"Autoregressive model","level":2,"score":0.41928496956825256},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2985433042049408},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.16785591840744019},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.0}],"mesh":[{"descriptor_ui":"D000465","descriptor_name":"Algorithms","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D000465","descriptor_name":"Algorithms","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D000465","descriptor_name":"Algorithms","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D004576","descriptor_name":"Electromyography","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D004576","descriptor_name":"Electromyography","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D004576","descriptor_name":"Electromyography","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D005542","descriptor_name":"Forearm","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D005542","descriptor_name":"Forearm","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D005542","descriptor_name":"Forearm","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D006801","descriptor_name":"Humans","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D006801","descriptor_name":"Humans","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D006801","descriptor_name":"Humans","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D010363","descriptor_name":"Pattern Recognition, Automated","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D010363","descriptor_name":"Pattern Recognition, Automated","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D010363","descriptor_name":"Pattern Recognition, Automated","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D025341","descriptor_name":"Principal Component Analysis","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D025341","descriptor_name":"Principal Component Analysis","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D025341","descriptor_name":"Principal Component Analysis","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false}],"locations_count":2,"locations":[{"id":"doi:10.1109/embc.2017.8037193","is_oa":false,"landing_page_url":"https://doi.org/10.1109/embc.2017.8037193","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)","raw_type":"proceedings-article"},{"id":"pmid:29060237","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/29060237","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference","raw_type":null}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":15,"referenced_works":["https://openalex.org/W885551291","https://openalex.org/W1736339626","https://openalex.org/W2034330155","https://openalex.org/W2037967616","https://openalex.org/W2048726136","https://openalex.org/W2097690077","https://openalex.org/W2103403570","https://openalex.org/W2109938769","https://openalex.org/W2120422653","https://openalex.org/W2144781570","https://openalex.org/W2154202969","https://openalex.org/W2793930808","https://openalex.org/W2955674036","https://openalex.org/W6749359388","https://openalex.org/W6765078354"],"related_works":["https://openalex.org/W1975632186","https://openalex.org/W3027745756","https://openalex.org/W3205213561","https://openalex.org/W2531880140","https://openalex.org/W2126145365","https://openalex.org/W2171218219","https://openalex.org/W2036609560","https://openalex.org/W1972271943","https://openalex.org/W346861917","https://openalex.org/W3024018414"],"abstract_inverted_index":{"Forearm":[0],"movements":[1,64],"realize":[2],"various":[3],"functions":[4],"needed":[5],"in":[6,29,47,100,134],"daily":[7],"life.":[8],"For":[9],"reproduction":[10],"of":[11,36,95,111,118],"the":[12,33,104,139],"motion":[13],"sequences,":[14],"active":[15],"myoelectric":[16,55,145],"devices":[17],"have":[18],"been":[19],"developed.":[20],"Usually,":[21],"feature":[22,56,136,140],"indices":[23,37,141],"are":[24,142],"extracted":[25],"from":[26,65],"observed":[27],"signals":[28],"control":[30],"strategy;":[31],"however,":[32],"optimal":[34,54],"combination":[35],"is":[38],"still":[39],"unclear.":[40],"This":[41],"paper":[42],"introduces":[43],"sparsity-inducing":[44],"penalty":[45],"term":[46],"principal":[48,119],"component":[49],"analysis":[50],"(PCA)":[51],"to":[52,131,144],"explore":[53],"indices.":[57],"An":[58],"electromyographic":[59],"database":[60],"including":[61],"seven":[62],"forearm":[63],"30":[66],"subjects":[67],"was":[68,85],"used":[69],"for":[70],"performance":[71,80],"comparison.":[72],"Linear":[73],"classifier":[74,90],"with":[75,91,115],"sparse":[76,105],"features":[77,93,106],"showed":[78],"best":[79],"(7.86\u00b13.82%":[81],"error":[82],"rate)":[83],"that":[84],"significantly":[86],"better":[87],"than":[88,121],"linear":[89],"all":[92],"because":[94],"recovering":[96],"low":[97],"rank":[98],"matrix":[99],"original":[101],"data.":[102],"Furthermore,":[103],"had":[107],"a":[108],"large":[109],"contribution":[110],"underlying":[112],"data":[113],"structure":[114],"less":[116],"number":[117],"components":[120],"PCA.":[122],"Root-mean-square,":[123],"time-domain":[124],"features,":[125],"autoregressive":[126],"coefficients,":[127],"and":[128],"Histogram":[129],"purported":[130],"be":[132],"important":[133,143],"projected":[135],"space;":[137],"therefore,":[138],"strategies.":[146]},"counts_by_year":[{"year":2020,"cited_by_count":1}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}