{"id":"https://openalex.org/W2921163450","doi":"https://doi.org/10.1109/robio.2018.8664796","title":"Prediction of EMG Signal on Missing Channel from Signal Captured from Other Related Channels via Deep Neural Network","display_name":"Prediction of EMG Signal on Missing Channel from Signal Captured from Other Related Channels via Deep Neural Network","publication_year":2018,"publication_date":"2018-12-01","ids":{"openalex":"https://openalex.org/W2921163450","doi":"https://doi.org/10.1109/robio.2018.8664796","mag":"2921163450"},"language":"en","primary_location":{"id":"doi:10.1109/robio.2018.8664796","is_oa":false,"landing_page_url":"https://doi.org/10.1109/robio.2018.8664796","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 IEEE International Conference on Robotics and Biomimetics (ROBIO)","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/A5101538675","display_name":"Wang Ping","orcid":"https://orcid.org/0009-0002-3022-6361"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ping Wang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5056723037","display_name":"Erlong Tan","orcid":"https://orcid.org/0009-0003-0604-1823"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Erlong Tan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101761946","display_name":"Yinli Jin","orcid":"https://orcid.org/0000-0002-2803-8989"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yinli Jin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5113168970","display_name":"Li Li","orcid":"https://orcid.org/0000-0001-5902-9762"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li Li","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5100384651","display_name":"Jun Wang","orcid":"https://orcid.org/0000-0001-8955-3530"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jun Wang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":5,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.446,"has_fulltext":false,"cited_by_count":9,"citation_normalized_percentile":{"value":0.63263689,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"1287","last_page":"1291"},"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":0.9975000023841858,"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/T10429","display_name":"EEG and Brain-Computer Interfaces","score":0.9973000288009644,"subfield":{"id":"https://openalex.org/subfields/2805","display_name":"Cognitive 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/signal","display_name":"SIGNAL (programming language)","score":0.7036186456680298},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.6170649528503418},{"id":"https://openalex.org/keywords/channel","display_name":"Channel (broadcasting)","score":0.616847038269043},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6027445197105408},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.4083724021911621},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4079809784889221},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3629840016365051},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.17333179712295532}],"concepts":[{"id":"https://openalex.org/C2779843651","wikidata":"https://www.wikidata.org/wiki/Q7390335","display_name":"SIGNAL (programming language)","level":2,"score":0.7036186456680298},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.6170649528503418},{"id":"https://openalex.org/C127162648","wikidata":"https://www.wikidata.org/wiki/Q16858953","display_name":"Channel (broadcasting)","level":2,"score":0.616847038269043},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6027445197105408},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.4083724021911621},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4079809784889221},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3629840016365051},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.17333179712295532},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/robio.2018.8664796","is_oa":false,"landing_page_url":"https://doi.org/10.1109/robio.2018.8664796","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 IEEE International Conference on Robotics and Biomimetics (ROBIO)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":22,"referenced_works":["https://openalex.org/W1970639711","https://openalex.org/W1989975862","https://openalex.org/W2036663497","https://openalex.org/W2062939404","https://openalex.org/W2072588720","https://openalex.org/W2095705004","https://openalex.org/W2100495367","https://openalex.org/W2121548891","https://openalex.org/W2126733169","https://openalex.org/W2130742238","https://openalex.org/W2161858945","https://openalex.org/W2173194528","https://openalex.org/W2365457334","https://openalex.org/W2726355887","https://openalex.org/W2743198946","https://openalex.org/W2752252465","https://openalex.org/W2762367260","https://openalex.org/W2787799858","https://openalex.org/W2794132617","https://openalex.org/W6674330103","https://openalex.org/W6679671233","https://openalex.org/W6684058330"],"related_works":["https://openalex.org/W2386387936","https://openalex.org/W2033914206","https://openalex.org/W2146076056","https://openalex.org/W2163831990","https://openalex.org/W2368779261","https://openalex.org/W3003836766","https://openalex.org/W2046077695","https://openalex.org/W2378160586","https://openalex.org/W2042327336","https://openalex.org/W2996038082"],"abstract_inverted_index":{"Capturing":[0],"electromyography":[1],"(EMG)":[2],"is":[3,62],"always":[4],"time-consuming":[5],"and":[6,16,64,88,98,112,152],"tedious":[7],"work":[8],"as":[9],"it":[10],"has":[11,120],"to":[12,26,126],"located":[13],"muscle":[14],"group":[15],"attach":[17],"the":[18,28,114,144,156],"electrode":[19],"with":[20,45,52,66,109,122,140],"proper":[21],"skin":[22],"preparation.":[23],"In":[24],"order":[25],"reduce":[27],"capturing":[29,139,157],"channel,":[30],"we":[31],"study":[32],"modeling":[33],"of":[34,54],"coordinated":[35],"muscles":[36,48,81],"in":[37],"this":[38],"paper.":[39],"Typical":[40],"movement":[41],"are":[42,76,107],"repetitively":[43],"conducted":[44],"seven":[46],"major":[47],"on":[49,72,105,155],"one":[50],"leg":[51],"help":[53],"recruited":[55],"participants.":[56],"A":[57],"deep":[58],"neural":[59],"network":[60],"(DNN)":[61],"proposed":[63],"trained":[65],"historical":[67],"data.":[68],"The":[69,101,117],"resulting":[70],"EMG":[71,103,138],"vastus":[73],"lateral":[74],"(VL)":[75],"then":[77],"predicted":[78,102,118],"from":[79,143],"other":[80,123],"including":[82],"rectus":[83],"femoris":[84,90],"(RF),":[85],"semitendinosus":[86],"(ST)":[87],"biceps":[89],"(BF),":[91],"tibialis":[92],"anterior":[93],"(TA),":[94],"glutaeus":[95],"maximus":[96],"(GM)":[97],"soleus":[99],"(SO).":[100],"signals":[104,142],"VL":[106],"compared":[108,121],"measuring":[110],"result":[111,119,131],"shows":[113],"high":[115],"accuracy.":[116],"learning-based":[124],"method":[125],"show":[127],"its":[128],"effectiveness.":[129],"This":[130],"can":[132],"be":[133],"used":[134],"for":[135],"less":[136],"channel":[137,146],"predicting":[141],"missing":[145],"which":[147],"will":[148],"save":[149],"experimental":[150],"time":[151],"money":[153],"investing":[154],"hardware.":[158]},"counts_by_year":[{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":2},{"year":2021,"cited_by_count":4}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
