{"id":"https://openalex.org/W7151406750","doi":"https://doi.org/10.1109/icmla66185.2025.00071","title":"Fusing Camera and Electromyography Data for Enhanced Range of Motion Assessment","display_name":"Fusing Camera and Electromyography Data for Enhanced Range of Motion Assessment","publication_year":2025,"publication_date":"2025-12-03","ids":{"openalex":"https://openalex.org/W7151406750","doi":"https://doi.org/10.1109/icmla66185.2025.00071"},"language":null,"primary_location":{"id":"doi:10.1109/icmla66185.2025.00071","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icmla66185.2025.00071","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 International Conference on Machine Learning and Applications (ICMLA)","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/A5020376195","display_name":"Xuke Yan","orcid":"https://orcid.org/0000-0002-1765-257X"},"institutions":[{"id":"https://openalex.org/I177721651","display_name":"Oakland University","ror":"https://ror.org/01ythxj32","country_code":"US","type":"education","lineage":["https://openalex.org/I177721651"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Xuke Yan","raw_affiliation_strings":["Oakland University,Rochester,MI,USA,48309"],"affiliations":[{"raw_affiliation_string":"Oakland University,Rochester,MI,USA,48309","institution_ids":["https://openalex.org/I177721651"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133065336","display_name":"Bo Liu","orcid":null},"institutions":[{"id":"https://openalex.org/I51158804","display_name":"Massey University","ror":"https://ror.org/052czxv31","country_code":"NZ","type":"education","lineage":["https://openalex.org/I51158804"]}],"countries":["NZ"],"is_corresponding":false,"raw_author_name":"Bo Liu","raw_affiliation_strings":["Massey University,Auckland,New Zealand,0745"],"affiliations":[{"raw_affiliation_string":"Massey University,Auckland,New Zealand,0745","institution_ids":["https://openalex.org/I51158804"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5034731073","display_name":"Jinzhao He","orcid":"https://orcid.org/0000-0001-6069-4364"},"institutions":[{"id":"https://openalex.org/I141945490","display_name":"University of British Columbia","ror":"https://ror.org/03rmrcq20","country_code":"CA","type":"education","lineage":["https://openalex.org/I141945490"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Jinzhao He","raw_affiliation_strings":["University Hill Secondary School,Vancouver,Canada,V6S 0C6"],"affiliations":[{"raw_affiliation_string":"University Hill Secondary School,Vancouver,Canada,V6S 0C6","institution_ids":["https://openalex.org/I141945490"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5084222400","display_name":"Guangzhi Qu","orcid":null},"institutions":[{"id":"https://openalex.org/I177721651","display_name":"Oakland University","ror":"https://ror.org/01ythxj32","country_code":"US","type":"education","lineage":["https://openalex.org/I177721651"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Guangzhi Qu","raw_affiliation_strings":["Oakland University,Rochester,MI,USA,48309"],"affiliations":[{"raw_affiliation_string":"Oakland University,Rochester,MI,USA,48309","institution_ids":["https://openalex.org/I177721651"]}]}],"institutions":[],"countries_distinct_count":3,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5020376195"],"corresponding_institution_ids":["https://openalex.org/I177721651"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.88846078,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"476","last_page":"483"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10114","display_name":"Balance, Gait, and Falls Prevention","score":0.22220000624656677,"subfield":{"id":"https://openalex.org/subfields/3612","display_name":"Physical Therapy, Sports Therapy and Rehabilitation"},"field":{"id":"https://openalex.org/fields/36","display_name":"Health Professions"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},"topics":[{"id":"https://openalex.org/T10114","display_name":"Balance, Gait, and Falls Prevention","score":0.22220000624656677,"subfield":{"id":"https://openalex.org/subfields/3612","display_name":"Physical Therapy, Sports Therapy and Rehabilitation"},"field":{"id":"https://openalex.org/fields/36","display_name":"Health Professions"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T10784","display_name":"Muscle activation and electromyography studies","score":0.21389999985694885,"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/T10510","display_name":"Stroke Rehabilitation and Recovery","score":0.041999999433755875,"subfield":{"id":"https://openalex.org/subfields/2742","display_name":"Rehabilitation"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/motion","display_name":"Motion (physics)","score":0.4528000056743622},{"id":"https://openalex.org/keywords/range","display_name":"Range (aeronautics)","score":0.43779999017715454},{"id":"https://openalex.org/keywords/electromyography","display_name":"Electromyography","score":0.43140000104904175},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.2888000011444092},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.2822999954223633}],"concepts":[{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.708899974822998},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6843000054359436},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5546000003814697},{"id":"https://openalex.org/C104114177","wikidata":"https://www.wikidata.org/wiki/Q79782","display_name":"Motion (physics)","level":2,"score":0.4528000056743622},{"id":"https://openalex.org/C204323151","wikidata":"https://www.wikidata.org/wiki/Q905424","display_name":"Range (aeronautics)","level":2,"score":0.43779999017715454},{"id":"https://openalex.org/C2777515770","wikidata":"https://www.wikidata.org/wiki/Q507369","display_name":"Electromyography","level":2,"score":0.43140000104904175},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.2888000011444092},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.2822999954223633},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.27709999680519104},{"id":"https://openalex.org/C33954974","wikidata":"https://www.wikidata.org/wiki/Q486494","display_name":"Sensor fusion","level":2,"score":0.26010000705718994},{"id":"https://openalex.org/C48007421","wikidata":"https://www.wikidata.org/wiki/Q676252","display_name":"Motion capture","level":3,"score":0.2524000108242035}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icmla66185.2025.00071","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icmla66185.2025.00071","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 International Conference on Machine Learning and Applications (ICMLA)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/7","score":0.4636361598968506,"display_name":"Affordable and clean energy"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":29,"referenced_works":["https://openalex.org/W2092777131","https://openalex.org/W2101032778","https://openalex.org/W2307770531","https://openalex.org/W2488164446","https://openalex.org/W2559085405","https://openalex.org/W2576289912","https://openalex.org/W2752782242","https://openalex.org/W2936823543","https://openalex.org/W2945419164","https://openalex.org/W2962858109","https://openalex.org/W2962899219","https://openalex.org/W2964062189","https://openalex.org/W2970413753","https://openalex.org/W3032925342","https://openalex.org/W3044180073","https://openalex.org/W3128469298","https://openalex.org/W3137555664","https://openalex.org/W3142504421","https://openalex.org/W3188493007","https://openalex.org/W4229073080","https://openalex.org/W4312871673","https://openalex.org/W4318475704","https://openalex.org/W4385763767","https://openalex.org/W4396782845","https://openalex.org/W4402952279","https://openalex.org/W4403537025","https://openalex.org/W4403675658","https://openalex.org/W4406705726","https://openalex.org/W4407736280"],"related_works":[],"abstract_inverted_index":{"Accurate":[0],"and":[1,14,26,47,98,118,136,146,162,167],"automated":[2],"Range":[3],"of":[4,160],"Motion":[5],"(ROM)":[6],"assessment":[7],"is":[8,61],"essential":[9],"for":[10,63,114,125],"rehabilitation,":[11],"physical":[12],"therapy,":[13],"post-surgical":[15],"recovery.":[16],"Traditional":[17],"manual":[18],"goniometer-based":[19],"evaluations":[20],"suffer":[21],"from":[22],"subjectivity,":[23],"inter-rater":[24],"variability,":[25],"reliance":[27],"on":[28],"trained":[29],"professionals.":[30],"Although":[31],"RGB-based":[32,80],"computer":[33],"vision":[34,54],"enables":[35],"markerless":[36],"ROM":[37,186],"estimation,":[38],"it":[39],"remains":[40],"susceptible":[41],"to":[42,101],"occlusions,":[43],"pose":[44],"estimation":[45],"errors,":[46],"difficulties":[48],"detecting":[49],"subtle":[50,102,185],"joint":[51,65,103],"movements.":[52,187],"Furthermore,":[53],"alone":[55],"cannot":[56],"capture":[57],"neuromuscular":[58,89],"activation,":[59],"which":[60],"crucial":[62],"understanding":[64],"dynamics.":[66],"To":[67,129],"address":[68],"these":[69],"challenges,":[70],"we":[71,133],"propose":[72],"a":[73,119],"multi-modal":[74],"deep":[75],"learning":[76],"framework":[77],"that":[78,152],"integrates":[79],"motion":[81],"tracking":[82],"with":[83],"electromyography":[84],"(EMG)":[85],"signals.":[86],"EMG":[87,127],"provides":[88],"activation":[90],"data,":[91],"enhancing":[92],"the":[93,142,165,173],"robustness":[94],"against":[95],"visual":[96],"occlusions":[97],"improving":[99],"sensitivity":[100],"displacements.":[104],"Our":[105],"method":[106],"employs":[107],"an":[108,157],"Hourglass-based":[109],"convolutional":[110],"neural":[111],"network":[112],"(CNN)":[113],"spatial":[115],"feature":[116,166],"extraction":[117],"gated":[120],"recurrent":[121],"unit":[122],"(GRU)-based":[123],"model":[124,155,177],"temporal":[126],"processing.":[128],"further":[130],"enhance":[131],"performance,":[132],"introduce":[134],"feature-level":[135],"modality-level":[137],"attention":[138,169],"modules,":[139],"dynamically":[140],"emphasizing":[141],"most":[143],"informative":[144],"features":[145],"modality":[147,168],"contributions.":[148],"Experimental":[149],"results":[150],"demonstrate":[151],"our":[153],"proposed":[154],"achieves":[156],"overall":[158],"RMSE":[159],"2.55,":[161],"improvements":[163],"via":[164],"mechanisms,":[170],"respectively.":[171],"Moreover,":[172],"fully":[174],"fused":[175],"RGB-EMG":[176],"outperforms":[178],"RGB-only":[179],"approaches,":[180],"particularly":[181],"in":[182],"accurately":[183],"predicting":[184]},"counts_by_year":[],"updated_date":"2026-04-09T06:08:40.794217","created_date":"2026-04-08T00:00:00"}
