{"id":"https://openalex.org/W7138985841","doi":"https://doi.org/10.48550/arxiv.2603.15625","title":"Parameter-Efficient Deep Learning for Ultrasound-Based Human-Machine Interfaces","display_name":"Parameter-Efficient Deep Learning for Ultrasound-Based Human-Machine Interfaces","publication_year":2026,"publication_date":"2026-02-09","ids":{"openalex":"https://openalex.org/W7138985841","doi":"https://doi.org/10.48550/arxiv.2603.15625"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2603.15625","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.15625","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2603.15625","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5092403912","display_name":"Antonios Lykourinas","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Lykourinas, Antonios","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5114513081","display_name":"Chinmay Pendse","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Pendse, Chinmay","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5033235745","display_name":"Francky Catthoor","orcid":"https://orcid.org/0000-0002-3599-8515"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Catthoor, Francky","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5033602331","display_name":"V\u00e9ronique Rochus","orcid":"https://orcid.org/0000-0001-9680-5724"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Rochus, Veronique","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5037079743","display_name":"Xavier Rottenberg","orcid":"https://orcid.org/0000-0003-0920-1709"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Rottenberg, Xavier","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5009555116","display_name":"Athanassios Skodras","orcid":"https://orcid.org/0000-0002-3872-4325"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Skodras, Athanassios","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5092403912"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10784","display_name":"Muscle activation and electromyography studies","score":0.5080000162124634,"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":0.5080000162124634,"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/T10653","display_name":"Robot Manipulation and Learning","score":0.12809999287128448,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems 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":0.06840000301599503,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.7587000131607056},{"id":"https://openalex.org/keywords/benchmarking","display_name":"Benchmarking","score":0.6502000093460083},{"id":"https://openalex.org/keywords/modality","display_name":"Modality (human\u2013computer interaction)","score":0.628600001335144},{"id":"https://openalex.org/keywords/preprocessor","display_name":"Preprocessor","score":0.5667999982833862},{"id":"https://openalex.org/keywords/modalities","display_name":"Modalities","score":0.4855000078678131},{"id":"https://openalex.org/keywords/iterative-learning-control","display_name":"Iterative learning control","score":0.4456999897956848},{"id":"https://openalex.org/keywords/robotics","display_name":"Robotics","score":0.392300009727478},{"id":"https://openalex.org/keywords/point","display_name":"Point (geometry)","score":0.3361000120639801}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.786300003528595},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.7587000131607056},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7145000100135803},{"id":"https://openalex.org/C86251818","wikidata":"https://www.wikidata.org/wiki/Q816754","display_name":"Benchmarking","level":2,"score":0.6502000093460083},{"id":"https://openalex.org/C2780226545","wikidata":"https://www.wikidata.org/wiki/Q6888030","display_name":"Modality (human\u2013computer interaction)","level":2,"score":0.628600001335144},{"id":"https://openalex.org/C34736171","wikidata":"https://www.wikidata.org/wiki/Q918333","display_name":"Preprocessor","level":2,"score":0.5667999982833862},{"id":"https://openalex.org/C2779903281","wikidata":"https://www.wikidata.org/wiki/Q6888026","display_name":"Modalities","level":2,"score":0.4855000078678131},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4830999970436096},{"id":"https://openalex.org/C117619785","wikidata":"https://www.wikidata.org/wiki/Q6094414","display_name":"Iterative learning control","level":3,"score":0.4456999897956848},{"id":"https://openalex.org/C34413123","wikidata":"https://www.wikidata.org/wiki/Q170978","display_name":"Robotics","level":3,"score":0.392300009727478},{"id":"https://openalex.org/C107457646","wikidata":"https://www.wikidata.org/wiki/Q207434","display_name":"Human\u2013computer interaction","level":1,"score":0.3377000093460083},{"id":"https://openalex.org/C28719098","wikidata":"https://www.wikidata.org/wiki/Q44946","display_name":"Point (geometry)","level":2,"score":0.3361000120639801},{"id":"https://openalex.org/C2775924081","wikidata":"https://www.wikidata.org/wiki/Q55608371","display_name":"Control (management)","level":2,"score":0.323199987411499},{"id":"https://openalex.org/C65155139","wikidata":"https://www.wikidata.org/wiki/Q5380912","display_name":"Envelope (radar)","level":3,"score":0.3116999864578247},{"id":"https://openalex.org/C113843644","wikidata":"https://www.wikidata.org/wiki/Q901882","display_name":"Interface (matter)","level":4,"score":0.3059999942779541},{"id":"https://openalex.org/C55439883","wikidata":"https://www.wikidata.org/wiki/Q360812","display_name":"Correctness","level":2,"score":0.30489999055862427},{"id":"https://openalex.org/C170130773","wikidata":"https://www.wikidata.org/wiki/Q216378","display_name":"Usability","level":2,"score":0.2939000129699707},{"id":"https://openalex.org/C208081375","wikidata":"https://www.wikidata.org/wiki/Q274502","display_name":"Degrees of freedom (physics and chemistry)","level":2,"score":0.28949999809265137},{"id":"https://openalex.org/C52102323","wikidata":"https://www.wikidata.org/wiki/Q1671968","display_name":"Pose","level":2,"score":0.2833000123500824},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.2669000029563904},{"id":"https://openalex.org/C175154964","wikidata":"https://www.wikidata.org/wiki/Q380077","display_name":"Task analysis","level":3,"score":0.2612999975681305}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2603.15625","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.15625","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2603.15625","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.15625","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/16","display_name":"Peace, Justice and strong institutions","score":0.7428101897239685}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Ultrasound":[0,87],"(US)":[1],"has":[2],"emerged":[3],"as":[4,138],"a":[5,62,126],"promising":[6,60],"modality":[7],"for":[8,19,36,185],"Human-Machine":[9],"Interfaces":[10],"(HMIs),":[11],"with":[12,33],"recent":[13],"research":[14],"efforts":[15],"exploring":[16],"its":[17],"potential":[18],"Hand":[20],"Pose":[21],"Estimation":[22],"(HPE).":[23],"A":[24],"reliable":[25],"solution":[26],"to":[27,38,171],"this":[28,101,119],"problem":[29],"could":[30],"introduce":[31],"interfaces":[32],"simultaneous":[34],"support":[35],"up":[37],"23":[39],"degrees":[40],"of":[41,65,107,134,164,177],"freedom":[42],"encompassing":[43],"all":[44],"hand":[45],"and":[46,53,69,96,131,180],"wrist":[47],"kinematics,":[48],"thereby":[49],"allowing":[50],"far":[51],"richer":[52],"more":[54],"intuitive":[55],"interaction":[56],"strategies.":[57],"Despite":[58],"these":[59],"results,":[61],"systematic":[63],"comparison":[64],"models,":[66,112],"input":[67,139],"modalities":[68],"training":[70,181],"strategies":[71],"is":[72,79,183],"missing":[73],"from":[74,166],"the":[75,86,105,132,135,174],"literature.":[76],"Moreover,":[77],"there":[78],"only":[80],"one":[81],"publicly":[82],"available":[83],"dataset,":[84,92],"namely":[85],"Adaptive":[88],"Prosthetic":[89],"Control":[90],"(Ultra-Pro)":[91],"enabling":[93],"reproducible":[94],"benchmarking":[95],"iterative":[97],"model":[98],"development.":[99],"In":[100],"paper,":[102],"we":[103],"compare":[104],"performance":[106,147],"six":[108],"different":[109],"deep":[110,143],"learning":[111,128],"selected":[113],"based":[114],"on":[115,118],"diverse":[116],"criteria,":[117],"benchmark.":[120],"We":[121],"demonstrate":[122],"that,":[123],"by":[124,148],"using":[125],"step":[127],"rate":[129],"scheduler":[130],"envelope":[133],"RF":[136],"signals":[137],"modality,":[140],"our":[141,172],"4-layer":[142],"UDACNN":[144],"surpasses":[145],"XceptionTime's":[146],"$2.28$":[149],"percentage":[150],"points":[151],"while":[152],"featuring":[153],"$87.52\\%$":[154],"fewer":[155],"parameters.":[156],"This":[157],"result":[158],"($77.72\\%$)":[159],"constitutes":[160],"an":[161],"absolute":[162],"improvement":[163],"$0.88\\%$":[165],"previously":[167],"reported":[168],"baselines.":[169],"According":[170],"findings,":[173],"appropriate":[175],"combination":[176],"model,":[178],"preprocessing":[179],"algorithm":[182],"crucial":[184],"optimizing":[186],"HMI":[187],"performance.":[188]},"counts_by_year":[],"updated_date":"2026-05-04T08:30:34.212998","created_date":"2026-03-20T00:00:00"}
