{"id":"https://openalex.org/W4401114118","doi":"https://doi.org/10.1109/tsp63128.2024.10605943","title":"Deep Learning Networks for Human Knee Abnormality Detection Based on Surface EMG Signals","display_name":"Deep Learning Networks for Human Knee Abnormality Detection Based on Surface EMG Signals","publication_year":2024,"publication_date":"2024-07-10","ids":{"openalex":"https://openalex.org/W4401114118","doi":"https://doi.org/10.1109/tsp63128.2024.10605943"},"language":"en","primary_location":{"id":"doi:10.1109/tsp63128.2024.10605943","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tsp63128.2024.10605943","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 47th International Conference on Telecommunications and Signal Processing (TSP)","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/A5105961490","display_name":"Sakorn Mekruksavanich","orcid":null},"institutions":[{"id":"https://openalex.org/I4210090662","display_name":"University of Phayao","ror":"https://ror.org/00a5mh069","country_code":"TH","type":"education","lineage":["https://openalex.org/I4210090662"]}],"countries":["TH"],"is_corresponding":true,"raw_author_name":"Sakorn Mekruksavanich","raw_affiliation_strings":["School of Information and Communication Technology, University of Phayao,Department of Computer Engineering,Phayao,Thailand"],"affiliations":[{"raw_affiliation_string":"School of Information and Communication Technology, University of Phayao,Department of Computer Engineering,Phayao,Thailand","institution_ids":["https://openalex.org/I4210090662"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5038701071","display_name":"Wikanda Phaphan","orcid":"https://orcid.org/0000-0002-6082-4779"},"institutions":[{"id":"https://openalex.org/I82828225","display_name":"King Mongkut's University of Technology North Bangkok","ror":"https://ror.org/04fy6jb97","country_code":"TH","type":"education","lineage":["https://openalex.org/I82828225"]}],"countries":["TH"],"is_corresponding":false,"raw_author_name":"Wikanda Phaphan","raw_affiliation_strings":["Research Group in Statistical Learning and Inference, King Mongkut&#x0027;s University of Technology North Bangkok,Faculty of Applied Science,Department of Applied Statistics,Bangkok,Thailand"],"affiliations":[{"raw_affiliation_string":"Research Group in Statistical Learning and Inference, King Mongkut&#x0027;s University of Technology North Bangkok,Faculty of Applied Science,Department of Applied Statistics,Bangkok,Thailand","institution_ids":["https://openalex.org/I82828225"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5085644461","display_name":"Anuchit Jitpattanakul","orcid":"https://orcid.org/0000-0002-5249-2786"},"institutions":[{"id":"https://openalex.org/I82828225","display_name":"King Mongkut's University of Technology North Bangkok","ror":"https://ror.org/04fy6jb97","country_code":"TH","type":"education","lineage":["https://openalex.org/I82828225"]}],"countries":["TH"],"is_corresponding":false,"raw_author_name":"Anuchit Jitpattanakul","raw_affiliation_strings":["Intelligent and Nonlinear Dynamic Innovations Research Center, King Mongkut&#x0027;s University of Technology North Bangkok,Department of Mathematics Faculty of Applied Science,Bangkok,Thailand"],"affiliations":[{"raw_affiliation_string":"Intelligent and Nonlinear Dynamic Innovations Research Center, King Mongkut&#x0027;s University of Technology North Bangkok,Department of Mathematics Faculty of Applied Science,Bangkok,Thailand","institution_ids":["https://openalex.org/I82828225"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5105961490"],"corresponding_institution_ids":["https://openalex.org/I4210090662"],"apc_list":null,"apc_paid":null,"fwci":0.5866,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.63127313,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":96,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"160","last_page":"163"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10784","display_name":"Muscle activation and electromyography studies","score":0.9991000294685364,"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":0.9991000294685364,"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/T11398","display_name":"Hand Gesture Recognition Systems","score":0.9983999729156494,"subfield":{"id":"https://openalex.org/subfields/1709","display_name":"Human-Computer Interaction"},"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/T11196","display_name":"Non-Invasive Vital Sign Monitoring","score":0.987500011920929,"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/abnormality","display_name":"Abnormality","score":0.7694435119628906},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6522799134254456},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5557650923728943},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5519992113113403},{"id":"https://openalex.org/keywords/surface","display_name":"Surface (topology)","score":0.4142530858516693},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.36149245500564575},{"id":"https://openalex.org/keywords/medicine","display_name":"Medicine","score":0.16253265738487244},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.07193952798843384}],"concepts":[{"id":"https://openalex.org/C50965678","wikidata":"https://www.wikidata.org/wiki/Q2724302","display_name":"Abnormality","level":2,"score":0.7694435119628906},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6522799134254456},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5557650923728943},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5519992113113403},{"id":"https://openalex.org/C2776799497","wikidata":"https://www.wikidata.org/wiki/Q484298","display_name":"Surface (topology)","level":2,"score":0.4142530858516693},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.36149245500564575},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.16253265738487244},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.07193952798843384},{"id":"https://openalex.org/C118552586","wikidata":"https://www.wikidata.org/wiki/Q7867","display_name":"Psychiatry","level":1,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tsp63128.2024.10605943","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tsp63128.2024.10605943","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 47th International Conference on Telecommunications and Signal Processing (TSP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G1946890873","display_name":null,"funder_award_id":"FF67-UoE-214","funder_id":"https://openalex.org/F4320326818","funder_display_name":"University of Phayao"},{"id":"https://openalex.org/G8056027561","display_name":null,"funder_award_id":"KMUTNB-FF-67-B-10","funder_id":"https://openalex.org/F4320324344","funder_display_name":"King Mongkut's University of Technology North Bangkok"}],"funders":[{"id":"https://openalex.org/F4320324344","display_name":"King Mongkut's University of Technology North Bangkok","ror":"https://ror.org/04fy6jb97"},{"id":"https://openalex.org/F4320326818","display_name":"University of Phayao","ror":"https://ror.org/00a5mh069"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":26,"referenced_works":["https://openalex.org/W2045488048","https://openalex.org/W2141633607","https://openalex.org/W2294168737","https://openalex.org/W2537975264","https://openalex.org/W2766614313","https://openalex.org/W2952135495","https://openalex.org/W2997199480","https://openalex.org/W3010103539","https://openalex.org/W3194178254","https://openalex.org/W3203762492","https://openalex.org/W4210718128","https://openalex.org/W4226383899","https://openalex.org/W4283013741","https://openalex.org/W4296400853","https://openalex.org/W4308078775","https://openalex.org/W4313201252","https://openalex.org/W4313585941","https://openalex.org/W4318587306","https://openalex.org/W4379876822","https://openalex.org/W4384557825","https://openalex.org/W4385277267","https://openalex.org/W4385498088","https://openalex.org/W4386362684","https://openalex.org/W4386824850","https://openalex.org/W4387587597","https://openalex.org/W4391914884"],"related_works":["https://openalex.org/W2731899572","https://openalex.org/W3215138031","https://openalex.org/W2772917594","https://openalex.org/W2036807459","https://openalex.org/W2058170566","https://openalex.org/W2755342338","https://openalex.org/W2166024367","https://openalex.org/W3116076068","https://openalex.org/W2229312674","https://openalex.org/W2951359407"],"abstract_inverted_index":{"Early":[0],"knee":[1,51,118],"problem":[2],"management":[3],"relies":[4],"on":[5,65],"precise":[6],"identification":[7],"and":[8,15,24,47,60,77,84,95,101,126,133],"classification":[9],"of":[10],"abnormalities.":[11],"Surface":[12],"electromyography":[13],"(sEMG)":[14],"goniometer":[16,48,96,127],"signals":[17],"offer":[18],"non-invasive":[19],"screening":[20],"for":[21,50],"muscle":[22],"activity":[23],"joint":[25],"angle":[26],"patterns,":[27],"yet":[28],"their":[29],"complexity":[30],"poses":[31],"challenges":[32],"in":[33,116],"extracting":[34],"critical":[35],"diagnostic":[36],"information.":[37],"This":[38],"paper":[39],"proposes":[40],"a":[41],"novel":[42],"deep-learning":[43],"method":[44],"using":[45,120],"sEMG":[46],"data":[49],"abnormality":[52],"diagnosis.":[53],"The":[54,111],"proposed":[55],"ResNeXt":[56],"model,":[57],"employing":[58],"CNNs":[59],"multi-kernel":[61],"modules,":[62],"is":[63],"evaluated":[64],"the":[66,89],"UCIEMG":[67],"dataset.":[68],"Experimental":[69],"results":[70],"demonstrate":[71],"ResNeXt's":[72,114],"superior":[73],"accuracy,":[74],"precision,":[75],"recall,":[76],"F1-score":[78],"compared":[79],"to":[80],"baseline":[81],"models":[82],"(CNN":[83],"LSTM).":[85],"Res":[86],"NeXt":[87],"achieves":[88],"best":[90],"performance":[91],"with":[92,104],"combined":[93],"EMG":[94],"data,":[97,122],"reaching":[98],"96.37%":[99],"accuracy":[100],"93.77%":[102],"F1-score,":[103],"fewer":[105],"trainable":[106],"parameters,":[107],"indicating":[108],"computational":[109],"efficiency.":[110],"findings":[112],"indicate":[113],"effectiveness":[115],"identifying":[117],"abnormalities":[119],"biosensor":[121],"particularly":[123],"sEM":[124],"G":[125],"signals,":[128],"aiding":[129],"early":[130],"disease":[131],"detection":[132],"treatment.":[134]},"counts_by_year":[{"year":2025,"cited_by_count":3}],"updated_date":"2025-12-21T01:58:51.020947","created_date":"2025-10-10T00:00:00"}
