{"id":"https://openalex.org/W4297804479","doi":"https://doi.org/10.1109/sas54819.2022.9881341","title":"Automatic Extraction of Muscle Fascicle Pennation Angle from Raw Ultrasound Data","display_name":"Automatic Extraction of Muscle Fascicle Pennation Angle from Raw Ultrasound Data","publication_year":2022,"publication_date":"2022-08-01","ids":{"openalex":"https://openalex.org/W4297804479","doi":"https://doi.org/10.1109/sas54819.2022.9881341"},"language":"en","primary_location":{"id":"doi:10.1109/sas54819.2022.9881341","is_oa":false,"landing_page_url":"https://doi.org/10.1109/sas54819.2022.9881341","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 IEEE Sensors Applications Symposium (SAS)","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/A5061572649","display_name":"Soley Hafthorsdottir","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Soley Hafthorsdottir","raw_affiliation_strings":["ETH Z&#x00FC;rich,Integrated Systems Laboratory,Z&#x00FC;rich,Switzerland"],"affiliations":[{"raw_affiliation_string":"ETH Z&#x00FC;rich,Integrated Systems Laboratory,Z&#x00FC;rich,Switzerland","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5066839454","display_name":"Sergei Vostrikov","orcid":"https://orcid.org/0000-0002-7927-5474"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sergei Vostrikov","raw_affiliation_strings":["ETH Z&#x00FC;rich,Integrated Systems Laboratory,Z&#x00FC;rich,Switzerland"],"affiliations":[{"raw_affiliation_string":"ETH Z&#x00FC;rich,Integrated Systems Laboratory,Z&#x00FC;rich,Switzerland","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5077996083","display_name":"Andrea Cossettini","orcid":"https://orcid.org/0000-0002-9621-3216"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Andrea Cossettini","raw_affiliation_strings":["ETH Z&#x00FC;rich,Integrated Systems Laboratory,Z&#x00FC;rich,Switzerland"],"affiliations":[{"raw_affiliation_string":"ETH Z&#x00FC;rich,Integrated Systems Laboratory,Z&#x00FC;rich,Switzerland","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5084898240","display_name":"Michael Rieder","orcid":"https://orcid.org/0000-0003-3079-2873"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Michael Rieder","raw_affiliation_strings":["ETH Z&#x00FC;rich,Center for Project Based Learning,Z&#x00FC;rich,Switzerland"],"affiliations":[{"raw_affiliation_string":"ETH Z&#x00FC;rich,Center for Project Based Learning,Z&#x00FC;rich,Switzerland","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5028925657","display_name":"Christoph Leitner","orcid":"https://orcid.org/0000-0002-7058-7236"},"institutions":[{"id":"https://openalex.org/I4092182","display_name":"Graz University of Technology","ror":"https://ror.org/00d7xrm67","country_code":"AT","type":"education","lineage":["https://openalex.org/I4092182"]}],"countries":["AT"],"is_corresponding":false,"raw_author_name":"Christoph Leitner","raw_affiliation_strings":["TU Graz,Institute of Health Care Engineering,Graz,Austria","Institute of Health Care Engineering, TU Graz, Graz, Austria"],"affiliations":[{"raw_affiliation_string":"TU Graz,Institute of Health Care Engineering,Graz,Austria","institution_ids":["https://openalex.org/I4092182"]},{"raw_affiliation_string":"Institute of Health Care Engineering, TU Graz, Graz, Austria","institution_ids":["https://openalex.org/I4092182"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5066423975","display_name":"Michele Magno","orcid":"https://orcid.org/0000-0003-0368-8923"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Michele Magno","raw_affiliation_strings":["ETH Z&#x00FC;rich,Center for Project Based Learning,Z&#x00FC;rich,Switzerland"],"affiliations":[{"raw_affiliation_string":"ETH Z&#x00FC;rich,Center for Project Based Learning,Z&#x00FC;rich,Switzerland","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5043408422","display_name":"Luca Benini","orcid":"https://orcid.org/0000-0001-8068-3806"},"institutions":[{"id":"https://openalex.org/I9360294","display_name":"University of Bologna","ror":"https://ror.org/01111rn36","country_code":"IT","type":"education","lineage":["https://openalex.org/I9360294"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Luca Benini","raw_affiliation_strings":["ETH Z&#x00FC;rich,Integrated Systems Laboratory,Z&#x00FC;rich,Switzerland","DEI, University of Bologna, Bologna, Italy"],"affiliations":[{"raw_affiliation_string":"ETH Z&#x00FC;rich,Integrated Systems Laboratory,Z&#x00FC;rich,Switzerland","institution_ids":[]},{"raw_affiliation_string":"DEI, University of Bologna, Bologna, Italy","institution_ids":["https://openalex.org/I9360294"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":7,"corresponding_author_ids":["https://openalex.org/A5061572649"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.2402,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.46315798,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":96},"biblio":{"volume":"12","issue":null,"first_page":"1","last_page":"5"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10784","display_name":"Muscle activation and electromyography studies","score":0.9990000128746033,"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":0.9990000128746033,"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/T11778","display_name":"Electrical and Bioimpedance Tomography","score":0.9948999881744385,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic 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":0.9915000200271606,"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/computer-science","display_name":"Computer science","score":0.7667093276977539},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6868100166320801},{"id":"https://openalex.org/keywords/wearable-computer","display_name":"Wearable computer","score":0.6141953468322754},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.5654740333557129},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.5429210066795349},{"id":"https://openalex.org/keywords/autoencoder","display_name":"Autoencoder","score":0.49299976229667664},{"id":"https://openalex.org/keywords/ground-truth","display_name":"Ground truth","score":0.47487685084342957},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4273437559604645},{"id":"https://openalex.org/keywords/frame-rate","display_name":"Frame rate","score":0.42144161462783813},{"id":"https://openalex.org/keywords/random-forest","display_name":"Random forest","score":0.419376015663147},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.34231728315353394}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7667093276977539},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6868100166320801},{"id":"https://openalex.org/C150594956","wikidata":"https://www.wikidata.org/wiki/Q1334829","display_name":"Wearable computer","level":2,"score":0.6141953468322754},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.5654740333557129},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5429210066795349},{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.49299976229667664},{"id":"https://openalex.org/C146849305","wikidata":"https://www.wikidata.org/wiki/Q370766","display_name":"Ground truth","level":2,"score":0.47487685084342957},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4273437559604645},{"id":"https://openalex.org/C3261483","wikidata":"https://www.wikidata.org/wiki/Q119565","display_name":"Frame rate","level":2,"score":0.42144161462783813},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.419376015663147},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.34231728315353394},{"id":"https://openalex.org/C149635348","wikidata":"https://www.wikidata.org/wiki/Q193040","display_name":"Embedded system","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/sas54819.2022.9881341","is_oa":false,"landing_page_url":"https://doi.org/10.1109/sas54819.2022.9881341","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 IEEE Sensors Applications Symposium (SAS)","raw_type":"proceedings-article"},{"id":"pmh:oai:cris.unibo.it:11585/907177","is_oa":false,"landing_page_url":"https://hdl.handle.net/11585/907177","pdf_url":null,"source":{"id":"https://openalex.org/S4306402579","display_name":"Archivio istituzionale della ricerca (Alma Mater Studiorum Universit\u00e0 di Bologna)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I4210117483","host_organization_name":"Istituto di Ematologia di Bologna","host_organization_lineage":["https://openalex.org/I4210117483"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"info:eu-repo/semantics/conferenceObject"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/15","display_name":"Life in Land","score":0.44999998807907104}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":28,"referenced_works":["https://openalex.org/W1836465849","https://openalex.org/W1947967821","https://openalex.org/W2011535805","https://openalex.org/W2101234009","https://openalex.org/W2127679821","https://openalex.org/W2145023731","https://openalex.org/W2153297196","https://openalex.org/W2161020449","https://openalex.org/W2295598076","https://openalex.org/W2753605315","https://openalex.org/W2907334801","https://openalex.org/W2908510526","https://openalex.org/W2968810166","https://openalex.org/W2978519582","https://openalex.org/W2993569504","https://openalex.org/W3005344222","https://openalex.org/W3010437941","https://openalex.org/W3011353253","https://openalex.org/W3011941870","https://openalex.org/W3100116152","https://openalex.org/W3128461475","https://openalex.org/W3213584935","https://openalex.org/W4287669229","https://openalex.org/W6638667902","https://openalex.org/W6675354045","https://openalex.org/W6744366274","https://openalex.org/W6757817989","https://openalex.org/W6783030576"],"related_works":["https://openalex.org/W2159052453","https://openalex.org/W3013693939","https://openalex.org/W2566616303","https://openalex.org/W3131327266","https://openalex.org/W2752972570","https://openalex.org/W4297051394","https://openalex.org/W2734887215","https://openalex.org/W2803255133","https://openalex.org/W2909431601","https://openalex.org/W4294770367"],"abstract_inverted_index":{"Compact,":[0],"wearable,":[1],"wireless":[2,52],"ultrasound":[3],"(US)":[4],"sensing":[5],"systems":[6],"are":[7,177],"promising":[8],"devices":[9],"for":[10,96,129,198,213],"the":[11,36,46,55,60,63,68,75,81,134,152,196,235,253,256,263],"observation":[12],"of":[13,23,41,50,71,137,154,166,246,255],"human":[14],"muscle":[15,139],"dynamics,":[16],"offering":[17],"low":[18,47],"power,":[19],"non-invasive":[20],"continuous":[21],"monitoring":[22,103],"contracting":[24,43,167],"muscles.":[25,44,170],"Plane":[26],"wave":[27],"imaging":[28,32,100],"is":[29,85,144,250],"an":[30],"ideal":[31],"modality":[33],"to":[34,62,87,116,182],"meet":[35],"high":[37],"frame":[38],"rate":[39],"requirements":[40,49],"fast":[42,99],"However,":[45],"power":[48],"wearable":[51],"US":[53,94,128,160,175,193],"constrain":[54],"data":[56,95,143,161],"transfer":[57],"rates":[58],"from":[59,92,141,158,174,191],"probe":[61],"host":[64],"computer,":[65],"and":[66,105,131,209,216,224,273],"limit":[67],"maximum":[69],"number":[70],"transducer":[72],"channels,":[73],"at":[74],"same":[76],"time":[77],"discouraging":[78],"beamforming":[79],"on":[80,162],"probe.":[82],"Therefore,":[83],"it":[84],"crucial":[86],"extract":[88,118],"physiological":[89],"parameters":[90],"directly":[91,190],"raw":[93,142,159,192],"applications":[97],"demanding":[98],"speeds":[101],"(like":[102],"muscles":[104],"tendons":[106],"in":[107,146,262],"motion).":[108],"Machine":[109],"Learning":[110],"(ML)":[111],"methods":[112],"can":[113],"be":[114],"employed":[115],"effectively":[117],"such":[119],"features.":[120],"Although":[121],"a":[122,163,242,266],"few":[123],"recent":[124],"works":[125],"demonstrated":[126],"A-mode":[127],"motion":[130],"force":[132],"prediction,":[133],"automatic":[135],"extraction":[136,215],"structural":[138],"features":[140],"still":[145],"its":[147],"infancy.":[148],"This":[149],"paper":[150],"demonstrates":[151],"feasibility":[153],"extracting":[155],"pennation":[156,188,259],"angles":[157,189,260],"small":[164],"dataset":[165],"medial":[168],"gastrocnemius":[169],"Automatically":[171],"extracted":[172],"labels":[173],"images":[176],"used":[178],"as":[179,229],"ground":[180],"truth":[181],"train":[183],"ML":[184],"algorithms":[185],"that":[186,234,249],"predict":[187],"data,":[194],"without":[195],"need":[197],"image":[199],"reconstruction.":[200],"We":[201],"employ":[202],"statistical":[203],"features,":[204],"Principle":[205],"Components":[206],"Analysis":[207],"(PCA)":[208],"Covolutional":[210],"Autoencoder":[211],"(AE)":[212],"feature":[214],"evaluate":[217],"Random":[218],"Forest":[219],"(RF),":[220],"Gradient":[221],"Boosting":[222],"(XGBoost)":[223],"Convoltional":[225],"Neural":[226],"Network":[227],"(CNN)":[228],"regressors.":[230],"Experimental":[231],"results":[232],"show":[233],"best":[236],"method":[237],"(AE":[238],"+":[239],"XGBoost)":[240],"achieves":[241],"mean":[243],"absolute":[244],"error":[245],"~":[247],"0.43\u00b0":[248],"consistent":[251],"with":[252,265],"variability":[254],"manually":[257],"annotated":[258],"reported":[261],"literature,":[264],"memory":[267],"footprint":[268],"smaller":[269],"than":[270,275],"400":[271],"kB":[272],"less":[274],"5":[276],"ms":[277],"execution":[278],"time.":[279]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
