{"id":"https://openalex.org/W4368405416","doi":"https://doi.org/10.3390/s23094449","title":"On the Early and Affordable Diagnosis of Joint Pathologies Using Acoustic Emissions, Deep Learning Decompositions and Prediction Machines","display_name":"On the Early and Affordable Diagnosis of Joint Pathologies Using Acoustic Emissions, Deep Learning Decompositions and Prediction Machines","publication_year":2023,"publication_date":"2023-05-02","ids":{"openalex":"https://openalex.org/W4368405416","doi":"https://doi.org/10.3390/s23094449","pmid":"https://pubmed.ncbi.nlm.nih.gov/37177652"},"language":"en","primary_location":{"id":"doi:10.3390/s23094449","is_oa":true,"landing_page_url":"https://doi.org/10.3390/s23094449","pdf_url":"https://www.mdpi.com/1424-8220/23/9/4449/pdf?version=1683020081","source":{"id":"https://openalex.org/S101949793","display_name":"Sensors","issn_l":"1424-8220","issn":["1424-8220"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Sensors","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj","pubmed"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.mdpi.com/1424-8220/23/9/4449/pdf?version=1683020081","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5026093225","display_name":"Ejay Nsugbe","orcid":null},"institutions":[{"id":"https://openalex.org/I4210087105","display_name":"UK Research and Innovation","ror":"https://ror.org/001aqnf71","country_code":"GB","type":"government","lineage":["https://openalex.org/I4210087105"]}],"countries":["GB"],"is_corresponding":true,"raw_author_name":"Ejay Nsugbe","raw_affiliation_strings":["Nsugbe Research Labs, Swindon SN1 3LG, UK"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Nsugbe Research Labs, Swindon SN1 3LG, UK","institution_ids":["https://openalex.org/I4210087105"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5023317690","display_name":"Khadijat A. Olorunlambe","orcid":"https://orcid.org/0000-0002-8192-7101"},"institutions":[{"id":"https://openalex.org/I79619799","display_name":"University of Birmingham","ror":"https://ror.org/03angcq70","country_code":"GB","type":"education","lineage":["https://openalex.org/I79619799"]}],"countries":["GB"],"is_corresponding":true,"raw_author_name":"Khadijat Olorunlambe","raw_affiliation_strings":["Mechanical Innovation and Tribology Group, Department of Mechanical Engineering, School of Engineering, University of Birmingham, Birmingham B15 2TT, UK"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Mechanical Innovation and Tribology Group, Department of Mechanical Engineering, School of Engineering, University of Birmingham, Birmingham B15 2TT, UK","institution_ids":["https://openalex.org/I79619799"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5084884518","display_name":"Karl D. Dearn","orcid":"https://orcid.org/0000-0002-8664-4303"},"institutions":[{"id":"https://openalex.org/I79619799","display_name":"University of Birmingham","ror":"https://ror.org/03angcq70","country_code":"GB","type":"education","lineage":["https://openalex.org/I79619799"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Karl Dearn","raw_affiliation_strings":["Mechanical Innovation and Tribology Group, Department of Mechanical Engineering, School of Engineering, University of Birmingham, Birmingham B15 2TT, UK"],"raw_orcid":"https://orcid.org/0000-0002-8664-4303","affiliations":[{"raw_affiliation_string":"Mechanical Innovation and Tribology Group, Department of Mechanical Engineering, School of Engineering, University of Birmingham, Birmingham B15 2TT, UK","institution_ids":["https://openalex.org/I79619799"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5023317690","https://openalex.org/A5026093225"],"corresponding_institution_ids":["https://openalex.org/I4210087105","https://openalex.org/I79619799"],"apc_list":{"value":2400,"currency":"CHF","value_usd":2598},"apc_paid":{"value":2400,"currency":"CHF","value_usd":2598},"fwci":0.6261,"has_fulltext":true,"cited_by_count":6,"citation_normalized_percentile":{"value":0.62973791,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":98},"biblio":{"volume":"23","issue":"9","first_page":"4449","last_page":"4449"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10784","display_name":"Muscle activation and electromyography studies","score":0.9679999947547913,"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":0.9679999947547913,"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/T12994","display_name":"Infrared Thermography in Medicine","score":0.9649999737739563,"subfield":{"id":"https://openalex.org/subfields/2741","display_name":"Radiology, Nuclear Medicine and Imaging"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T10105","display_name":"Osteoarthritis Treatment and Mechanisms","score":0.9571999907493591,"subfield":{"id":"https://openalex.org/subfields/2745","display_name":"Rheumatology"},"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/computer-science","display_name":"Computer science","score":0.6266885995864868},{"id":"https://openalex.org/keywords/joint","display_name":"Joint (building)","score":0.591620683670044},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5699010491371155},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.56052565574646},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4898516833782196},{"id":"https://openalex.org/keywords/feature-engineering","display_name":"Feature engineering","score":0.48485028743743896},{"id":"https://openalex.org/keywords/modality","display_name":"Modality (human\u2013computer interaction)","score":0.4649193584918976},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4635213315486908},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.4374505579471588},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.40863069891929626},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.17178979516029358}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6266885995864868},{"id":"https://openalex.org/C18555067","wikidata":"https://www.wikidata.org/wiki/Q8375051","display_name":"Joint (building)","level":2,"score":0.591620683670044},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5699010491371155},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.56052565574646},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4898516833782196},{"id":"https://openalex.org/C2778827112","wikidata":"https://www.wikidata.org/wiki/Q22245680","display_name":"Feature engineering","level":3,"score":0.48485028743743896},{"id":"https://openalex.org/C2780226545","wikidata":"https://www.wikidata.org/wiki/Q6888030","display_name":"Modality (human\u2013computer interaction)","level":2,"score":0.4649193584918976},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4635213315486908},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.4374505579471588},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.40863069891929626},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.17178979516029358},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C170154142","wikidata":"https://www.wikidata.org/wiki/Q150737","display_name":"Architectural engineering","level":1,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0}],"mesh":[{"descriptor_ui":"D000069550","descriptor_name":"Machine Learning","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D000069550","descriptor_name":"Machine Learning","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D000069550","descriptor_name":"Machine Learning","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D000077321","descriptor_name":"Deep Learning","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D000077321","descriptor_name":"Deep Learning","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D000077321","descriptor_name":"Deep Learning","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D000162","descriptor_name":"Acoustics","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D000162","descriptor_name":"Acoustics","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D000162","descriptor_name":"Acoustics","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D000368","descriptor_name":"Aged","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D000368","descriptor_name":"Aged","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D000368","descriptor_name":"Aged","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D006801","descriptor_name":"Humans","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D006801","descriptor_name":"Humans","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D006801","descriptor_name":"Humans","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D008279","descriptor_name":"Magnetic Resonance Imaging","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D008279","descriptor_name":"Magnetic Resonance Imaging","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D008279","descriptor_name":"Magnetic Resonance Imaging","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false}],"locations_count":7,"locations":[{"id":"doi:10.3390/s23094449","is_oa":true,"landing_page_url":"https://doi.org/10.3390/s23094449","pdf_url":"https://www.mdpi.com/1424-8220/23/9/4449/pdf?version=1683020081","source":{"id":"https://openalex.org/S101949793","display_name":"Sensors","issn_l":"1424-8220","issn":["1424-8220"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Sensors","raw_type":"journal-article"},{"id":"pmid:37177652","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/37177652","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Sensors (Basel, Switzerland)","raw_type":null},{"id":"pmh:oai:pubmedcentral.nih.gov:10181577","is_oa":true,"landing_page_url":"https://www.ncbi.nlm.nih.gov/pmc/articles/10181577","pdf_url":"https://pmc.ncbi.nlm.nih.gov/articles/PMC10181577/pdf/sensors-23-04449.pdf","source":{"id":"https://openalex.org/S2764455111","display_name":"PubMed Central","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Sensors (Basel)","raw_type":"Text"},{"id":"pmh:oai:pure.atira.dk:publications/f178e85d-d759-4f32-b1c0-9ec8e7883fee","is_oa":true,"landing_page_url":"https://research.birmingham.ac.uk/en/publications/f178e85d-d759-4f32-b1c0-9ec8e7883fee","pdf_url":"https://pure-oai.bham.ac.uk/ws/files/196138600/sensors-23-04449.pdf","source":{"id":"https://openalex.org/S4306402634","display_name":"University of Birmingham Research Portal (University of Birmingham)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I79619799","host_organization_name":"University of Birmingham","host_organization_lineage":["https://openalex.org/I79619799"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Nsugbe , E , Olorunlambe , K & Dearn , K 2023 , ' On the Early and Affordable Diagnosis of Joint Pathologies Using Acoustic Emissions, Deep Learning Decompositions and Prediction Machines ' , Sensors , vol. 23 , no. 9 , 4449 . https://doi.org/10.3390/s23094449","raw_type":"article"},{"id":"pmh:oai:doaj.org/article:32b483636c084b2bb5ffc5c38c717236","is_oa":true,"landing_page_url":"https://doaj.org/article/32b483636c084b2bb5ffc5c38c717236","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Sensors, Vol 23, Iss 9, p 4449 (2023)","raw_type":"article"},{"id":"pmh:oai:mdpi.com:/1424-8220/23/9/4449/","is_oa":true,"landing_page_url":"https://dx.doi.org/10.3390/s23094449","pdf_url":null,"source":{"id":"https://openalex.org/S4306400947","display_name":"MDPI (MDPI AG)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I4210097602","host_organization_name":"Multidisciplinary Digital Publishing Institute (Switzerland)","host_organization_lineage":["https://openalex.org/I4210097602"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Sensors; Volume 23; Issue 9; Pages: 4449","raw_type":"Text"},{"id":"pmh:oai:pure.atira.dk:openaire_cris_publications/f178e85d-d759-4f32-b1c0-9ec8e7883fee","is_oa":true,"landing_page_url":"https://research.birmingham.ac.uk/files/196138600/sensors-23-04449.pdf","pdf_url":null,"source":{"id":"https://openalex.org/S4306402634","display_name":"University of Birmingham Research Portal (University of Birmingham)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I79619799","host_organization_name":"University of Birmingham","host_organization_lineage":["https://openalex.org/I79619799"],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Nsugbe , E , Olorunlambe , K & Dearn , K 2023 , ' On the Early and Affordable Diagnosis of Joint Pathologies Using Acoustic Emissions, Deep Learning Decompositions and Prediction Machines ' , Sensors , vol. 23 , no. 9 , 4449 . https://doi.org/10.3390/s23094449","raw_type":"article"}],"best_oa_location":{"id":"doi:10.3390/s23094449","is_oa":true,"landing_page_url":"https://doi.org/10.3390/s23094449","pdf_url":"https://www.mdpi.com/1424-8220/23/9/4449/pdf?version=1683020081","source":{"id":"https://openalex.org/S101949793","display_name":"Sensors","issn_l":"1424-8220","issn":["1424-8220"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Sensors","raw_type":"journal-article"},"sustainable_development_goals":[{"score":0.5299999713897705,"display_name":"Decent work and economic growth","id":"https://metadata.un.org/sdg/8"}],"awards":[{"id":"https://openalex.org/G3972399002","display_name":null,"funder_award_id":"EP/R041407/1","funder_id":"https://openalex.org/F4320334627","funder_display_name":"Engineering and Physical Sciences Research Council"},{"id":"https://openalex.org/G7445246775","display_name":null,"funder_award_id":"EP/L017725/1","funder_id":"https://openalex.org/F4320334627","funder_display_name":"Engineering and Physical Sciences Research Council"}],"funders":[{"id":"https://openalex.org/F4320334627","display_name":"Engineering and Physical Sciences Research Council","ror":"https://ror.org/0439y7842"}],"has_content":{"grobid_xml":false,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4368405416.pdf"},"referenced_works_count":39,"referenced_works":["https://openalex.org/W1994906459","https://openalex.org/W2004253480","https://openalex.org/W2041328954","https://openalex.org/W2042810717","https://openalex.org/W2046592845","https://openalex.org/W2057019319","https://openalex.org/W2093231248","https://openalex.org/W2137210230","https://openalex.org/W2165187311","https://openalex.org/W2166636227","https://openalex.org/W2170348081","https://openalex.org/W2479517029","https://openalex.org/W2573003069","https://openalex.org/W2574388714","https://openalex.org/W2621357895","https://openalex.org/W2752387010","https://openalex.org/W2756344646","https://openalex.org/W2791495114","https://openalex.org/W2803914058","https://openalex.org/W2897461353","https://openalex.org/W2903483619","https://openalex.org/W2948142742","https://openalex.org/W2963825195","https://openalex.org/W3034800277","https://openalex.org/W3037102754","https://openalex.org/W3136821121","https://openalex.org/W3140016708","https://openalex.org/W3148181069","https://openalex.org/W3153065345","https://openalex.org/W3167541708","https://openalex.org/W3196827187","https://openalex.org/W3213916406","https://openalex.org/W4200128022","https://openalex.org/W4225848842","https://openalex.org/W4229063342","https://openalex.org/W4280652252","https://openalex.org/W4285900037","https://openalex.org/W6752029374","https://openalex.org/W6796917555"],"related_works":["https://openalex.org/W3199964822","https://openalex.org/W3048601286","https://openalex.org/W3034267371","https://openalex.org/W2965925734","https://openalex.org/W4232132981","https://openalex.org/W4238046985","https://openalex.org/W4283320496","https://openalex.org/W3164948662","https://openalex.org/W3003242282","https://openalex.org/W3153597579"],"abstract_inverted_index":{"The":[0,71,153],"condition":[1,28,202,215],"of":[2,29,55,65,73,98,129,135,160,190,199,209,217],"a":[3,6,30,35,49,78,133,200],"joint":[4,31],"in":[5,17,58,219],"human":[7,220],"being":[8],"is":[9,107],"prone":[10],"to":[11,32,47,68,114,125],"wear":[12,201],"and":[13,20,43,61,109,139,164,171,197],"several":[14],"pathologies,":[15],"particularly":[16],"the":[18,26,63,69,83,89,99,169,179,187,194,214],"elderly":[19],"athletes.":[21],"Current":[22],"means":[23],"towards":[24,192],"assessing":[25],"overall":[27],"assess":[33],"for":[34,168,213],"pathology":[36],"involve":[37,186],"using":[38,132],"tools":[39],"such":[40],"as":[41],"X-ray":[42],"magnetic":[44],"resonance":[45],"imaging,":[46],"name":[48],"couple.":[50],"These":[51],"expensive":[52],"methods":[53],"are":[54],"limited":[56],"availability":[57],"resource-constrained":[59],"environments":[60],"pose":[62],"risk":[64],"radiation":[66],"exposure":[67],"patient.":[70],"prospect":[72],"acoustic":[74],"emissions":[75],"(AEs)":[76],"presents":[77],"modality":[79],"that":[80],"can":[81,206],"monitor":[82],"joints'":[84],"conditions":[85],"passively":[86],"by":[87],"recording":[88],"high-frequency":[90],"stress":[91],"waves":[92],"emitted":[93],"during":[94],"their":[95,115],"motion.":[96],"One":[97],"main":[100],"challenges":[101],"associated":[102,195],"with":[103],"this":[104,119],"sensing":[105],"method":[106],"decoding":[108],"linking":[110],"acquired":[111],"AE":[112],"signals":[113],"source":[116],"event.":[117],"In":[118],"paper,":[120],"we":[121],"investigate":[122],"AEs'":[123],"use":[124],"identify":[126],"five":[127],"kinds":[128],"joint-wear":[130],"pathologies":[131],"contrast":[134],"expert-based":[136],"handcrafted":[137,170],"features":[138],"unsupervised":[140],"feature":[141],"learning":[142,151],"via":[143],"deep":[144],"wavelet":[145],"decomposition":[146],"(DWS)":[147],"alongside":[148],"12":[149],"machine":[150],"models.":[152],"results":[154],"showed":[155],"an":[156,210],"average":[157],"classification":[158],"accuracy":[159],"90":[161],"\u00b1":[162,166],"7.16%":[163],"97":[165],"3.77%":[167],"DWS-based":[172],"features,":[173],"implying":[174],"good":[175],"prediction":[176],"accuracies":[177],"across":[178],"various":[180],"devised":[181],"approaches.":[182],"Subsequent":[183],"work":[184],"will":[185],"potential":[188],"application":[189],"regressions":[191],"estimating":[193],"stage":[196],"extent":[198],"where":[203],"present,":[204],"which":[205],"form":[207],"part":[208],"online":[211],"system":[212],"monitoring":[216],"joints":[218],"beings.":[221]},"counts_by_year":[{"year":2025,"cited_by_count":4},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1}],"updated_date":"2026-05-21T06:26:12.895304","created_date":"2025-10-10T00:00:00"}