{"id":"https://openalex.org/W7125961211","doi":"https://doi.org/10.1109/smc58881.2025.11343284","title":"Motion Intention Decoding: The Role of Data Parameters in Motor Unit-Based Decoders","display_name":"Motion Intention Decoding: The Role of Data Parameters in Motor Unit-Based Decoders","publication_year":2025,"publication_date":"2025-10-05","ids":{"openalex":"https://openalex.org/W7125961211","doi":"https://doi.org/10.1109/smc58881.2025.11343284"},"language":null,"primary_location":{"id":"doi:10.1109/smc58881.2025.11343284","is_oa":false,"landing_page_url":"https://doi.org/10.1109/smc58881.2025.11343284","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE International Conference on Systems, Man, and Cybernetics (SMC)","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/A5124103087","display_name":"Long Meng","orcid":null},"institutions":[{"id":"https://openalex.org/I130769515","display_name":"Pennsylvania State University","ror":"https://ror.org/04p491231","country_code":"US","type":"education","lineage":["https://openalex.org/I130769515"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Long Meng","raw_affiliation_strings":["Pennsylvania State University,Department of Mechanical Engineering,University Park,PA,USA"],"affiliations":[{"raw_affiliation_string":"Pennsylvania State University,Department of Mechanical Engineering,University Park,PA,USA","institution_ids":["https://openalex.org/I130769515"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5122853206","display_name":"Xiaogang Hu","orcid":null},"institutions":[{"id":"https://openalex.org/I130769515","display_name":"Pennsylvania State University","ror":"https://ror.org/04p491231","country_code":"US","type":"education","lineage":["https://openalex.org/I130769515"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Xiaogang Hu","raw_affiliation_strings":["Pennsylvania State University,Department of Mechanical Engineering,University Park,PA,USA"],"affiliations":[{"raw_affiliation_string":"Pennsylvania State University,Department of Mechanical Engineering,University Park,PA,USA","institution_ids":["https://openalex.org/I130769515"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5124103087"],"corresponding_institution_ids":["https://openalex.org/I130769515"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.61832551,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"4326","last_page":"4329"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10784","display_name":"Muscle activation and electromyography studies","score":0.49140000343322754,"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":0.49140000343322754,"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":0.29910001158714294,"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"}},{"id":"https://openalex.org/T10982","display_name":"Motor Control and Adaptation","score":0.14000000059604645,"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/decoding-methods","display_name":"Decoding methods","score":0.8715000152587891},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.6629999876022339},{"id":"https://openalex.org/keywords/pipeline","display_name":"Pipeline (software)","score":0.6179999709129333},{"id":"https://openalex.org/keywords/neural-decoding","display_name":"Neural decoding","score":0.49480000138282776},{"id":"https://openalex.org/keywords/motion","display_name":"Motion (physics)","score":0.4932999908924103},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.453000009059906},{"id":"https://openalex.org/keywords/interface","display_name":"Interface (matter)","score":0.4117000102996826},{"id":"https://openalex.org/keywords/encoding","display_name":"Encoding (memory)","score":0.40610000491142273},{"id":"https://openalex.org/keywords/robotics","display_name":"Robotics","score":0.38190001249313354}],"concepts":[{"id":"https://openalex.org/C57273362","wikidata":"https://www.wikidata.org/wiki/Q576722","display_name":"Decoding methods","level":2,"score":0.8715000152587891},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7354000210762024},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6804999709129333},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.6629999876022339},{"id":"https://openalex.org/C43521106","wikidata":"https://www.wikidata.org/wiki/Q2165493","display_name":"Pipeline (software)","level":2,"score":0.6179999709129333},{"id":"https://openalex.org/C40743351","wikidata":"https://www.wikidata.org/wiki/Q7002049","display_name":"Neural decoding","level":3,"score":0.49480000138282776},{"id":"https://openalex.org/C104114177","wikidata":"https://www.wikidata.org/wiki/Q79782","display_name":"Motion (physics)","level":2,"score":0.4932999908924103},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.47380000352859497},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.453000009059906},{"id":"https://openalex.org/C113843644","wikidata":"https://www.wikidata.org/wiki/Q901882","display_name":"Interface (matter)","level":4,"score":0.4117000102996826},{"id":"https://openalex.org/C125411270","wikidata":"https://www.wikidata.org/wiki/Q18653","display_name":"Encoding (memory)","level":2,"score":0.40610000491142273},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.38679999113082886},{"id":"https://openalex.org/C34413123","wikidata":"https://www.wikidata.org/wiki/Q170978","display_name":"Robotics","level":3,"score":0.38190001249313354},{"id":"https://openalex.org/C102392041","wikidata":"https://www.wikidata.org/wiki/Q592860","display_name":"Sliding window protocol","level":3,"score":0.3788999915122986},{"id":"https://openalex.org/C103486182","wikidata":"https://www.wikidata.org/wiki/Q1216236","display_name":"Isometric exercise","level":2,"score":0.3359000086784363},{"id":"https://openalex.org/C48007421","wikidata":"https://www.wikidata.org/wiki/Q676252","display_name":"Motion capture","level":3,"score":0.3156000077724457},{"id":"https://openalex.org/C2777515770","wikidata":"https://www.wikidata.org/wiki/Q507369","display_name":"Electromyography","level":2,"score":0.30869999527931213},{"id":"https://openalex.org/C90509273","wikidata":"https://www.wikidata.org/wiki/Q11012","display_name":"Robot","level":2,"score":0.3057999908924103},{"id":"https://openalex.org/C145460709","wikidata":"https://www.wikidata.org/wiki/Q859951","display_name":"Human\u2013robot interaction","level":3,"score":0.302700012922287},{"id":"https://openalex.org/C173201364","wikidata":"https://www.wikidata.org/wiki/Q897410","display_name":"Brain\u2013computer interface","level":3,"score":0.2946000099182129},{"id":"https://openalex.org/C146549078","wikidata":"https://www.wikidata.org/wiki/Q191944","display_name":"Exoskeleton","level":2,"score":0.2632000148296356},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.26190000772476196},{"id":"https://openalex.org/C2986578859","wikidata":"https://www.wikidata.org/wiki/Q657632","display_name":"Human motion","level":3,"score":0.25769999623298645},{"id":"https://openalex.org/C23224414","wikidata":"https://www.wikidata.org/wiki/Q176769","display_name":"Hidden Markov model","level":2,"score":0.2574000060558319},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.2549000084400177}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/smc58881.2025.11343284","is_oa":false,"landing_page_url":"https://doi.org/10.1109/smc58881.2025.11343284","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE International Conference on Systems, Man, and Cybernetics (SMC)","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":16,"referenced_works":["https://openalex.org/W2019502123","https://openalex.org/W2587122088","https://openalex.org/W2936886914","https://openalex.org/W3121789984","https://openalex.org/W3124139391","https://openalex.org/W4220863867","https://openalex.org/W4312701680","https://openalex.org/W4384700931","https://openalex.org/W4388666098","https://openalex.org/W4393238105","https://openalex.org/W4400878455","https://openalex.org/W4404914652","https://openalex.org/W4405709291","https://openalex.org/W4407168890","https://openalex.org/W4408564790","https://openalex.org/W4410877507"],"related_works":[],"abstract_inverted_index":{"Accurate":[0],"decoding":[1,37,69,117,145,198,222],"of":[2,32,61,86,95,130,175,205],"human":[3,106],"motion":[4,220],"intention":[5,221],"from":[6,13,104,163],"surface":[7,16],"electromyography":[8],"(sEMG)":[9],"signals":[10,88,101],"recorded":[11,103],"non-invasively":[12],"the":[14,30,59,66,80,93,128,173,192,206],"skin":[15],"is":[17],"critical":[18],"for":[19,90,121,214],"enabling":[20],"intuitive":[21],"control":[22],"in":[23,218],"assistive":[24],"robotics":[25],"and":[26,68,83,110,141,147],"human\u2013machine":[27],"interactions.":[28],"With":[29],"advancement":[31],"high-density":[33],"sEMG":[34,87],"(HD-sEMG),":[35],"neural":[36,116],"methods":[38],"based":[39],"on":[40,65,144],"motor":[41],"unit":[42],"(MU)":[43],"activity":[44],"have":[45],"shown":[46],"promise":[47],"due":[48],"to":[49,52,155,201],"their":[50],"potential":[51],"capture":[53],"finely":[54],"controlled":[55],"movement":[56],"information.":[57],"However,":[58],"effects":[60],"data":[62,84,177,185,216],"segmentation":[63,81,132],"parameters":[64],"decomposition":[67,91],"accuracy":[70],"remain":[71],"underexplored.":[72],"In":[73],"this":[74,168],"study,":[75],"we":[76,170],"systematically":[77],"investigated":[78],"how":[79],"length":[82],"size":[85,178],"used":[89],"affect":[92],"performance":[94,199],"finger":[96,122],"force":[97,113,123],"decoding.":[98],"Specifically,":[99],"HD-sEMG":[100],"were":[102],"eight":[105],"participants":[107],"during":[108],"single-":[109],"multi-finger":[111],"isometric":[112],"tasks.":[114],"A":[115],"pipeline":[118],"was":[119,153],"developed":[120],"predictions.":[124],"We":[125],"first":[126],"evaluated":[127],"impact":[129],"four":[131],"window":[133,152],"lengths":[134],"(10":[135],"s,":[136,138,140],"20":[137],"40":[139],"80":[142],"s)":[143],"accuracy,":[146],"found":[148],"that":[149,190],"a":[150],"20-second":[151],"sufficient":[154],"ensure":[156],"accurate":[157],"decoding,":[158],"with":[159,183],"no":[160],"additional":[161],"benefit":[162],"using":[164,191,202],"longer":[165],"segments.":[166],"Using":[167],"setting,":[169],"further":[171],"examined":[172],"effect":[174],"training":[176,194,207],"by":[179],"comparing":[180],"decoders":[181],"trained":[182],"different":[184],"sizes.":[186],"Our":[187],"results":[188],"showed":[189],"full":[193],"dataset":[195],"significantly":[196],"improved":[197],"compared":[200],"only":[203],"half":[204],"dataset.":[208],"These":[209],"findings":[210],"offer":[211],"practical":[212],"guidelines":[213],"optimizing":[215],"usage":[217],"MU-based":[219],"systems.":[223]},"counts_by_year":[],"updated_date":"2026-01-29T23:17:01.242718","created_date":"2026-01-29T00:00:00"}