{"id":"https://openalex.org/W2086099262","doi":"https://doi.org/10.1142/s1469026808002314","title":"SVM MODELS FOR DIAGNOSING BALANCE PROBLEMS USING STATISTICAL FEATURES OF THE MTC SIGNAL","display_name":"SVM MODELS FOR DIAGNOSING BALANCE PROBLEMS USING STATISTICAL FEATURES OF THE MTC SIGNAL","publication_year":2008,"publication_date":"2008-09-01","ids":{"openalex":"https://openalex.org/W2086099262","doi":"https://doi.org/10.1142/s1469026808002314","mag":"2086099262"},"language":"en","primary_location":{"id":"doi:10.1142/s1469026808002314","is_oa":false,"landing_page_url":"https://doi.org/10.1142/s1469026808002314","pdf_url":null,"source":{"id":"https://openalex.org/S206936884","display_name":"International Journal of Computational Intelligence and Applications","issn_l":"1469-0268","issn":["1469-0268","1757-5885"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310311754","host_organization_name":"Imperial College Press","host_organization_lineage":["https://openalex.org/P4310311754"],"host_organization_lineage_names":["Imperial College Press"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"International Journal of Computational Intelligence and Applications","raw_type":"journal-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/A5087639822","display_name":"Daniel Lai","orcid":"https://orcid.org/0000-0003-3459-7709"},"institutions":[{"id":"https://openalex.org/I165779595","display_name":"University of Melbourne","ror":"https://ror.org/01ej9dk98","country_code":"AU","type":"education","lineage":["https://openalex.org/I165779595"]}],"countries":["AU"],"is_corresponding":true,"raw_author_name":"DANIEL T. H. LAI","raw_affiliation_strings":["Department of Electrical Engineering, The University of Melbourne, Parkville Campus, Melbourne, Victoria 3010, Australia"],"affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering, The University of Melbourne, Parkville Campus, Melbourne, Victoria 3010, Australia","institution_ids":["https://openalex.org/I165779595"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5084723266","display_name":"Rezaul Begg","orcid":"https://orcid.org/0000-0002-3195-8591"},"institutions":[{"id":"https://openalex.org/I71270174","display_name":"Victoria University","ror":"https://ror.org/04j757h98","country_code":"AU","type":"education","lineage":["https://openalex.org/I71270174"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"REZAUL BEGG","raw_affiliation_strings":["Centre for Ageing, Rehabilitation, Exercise and Sport, Victoria University, Melbourne, Victoria 8001, Australia"],"affiliations":[{"raw_affiliation_string":"Centre for Ageing, Rehabilitation, Exercise and Sport, Victoria University, Melbourne, Victoria 8001, Australia","institution_ids":["https://openalex.org/I71270174"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5080554686","display_name":"Marimuthu Palaniswami","orcid":"https://orcid.org/0000-0002-3635-4252"},"institutions":[{"id":"https://openalex.org/I165779595","display_name":"University of Melbourne","ror":"https://ror.org/01ej9dk98","country_code":"AU","type":"education","lineage":["https://openalex.org/I165779595"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"MARIMUTHU PALANISWAMI","raw_affiliation_strings":["Department of Electrical Engineering, The University of Melbourne, Parkville Campus, Melbourne, Victoria 3010, Australia"],"affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering, The University of Melbourne, Parkville Campus, Melbourne, Victoria 3010, Australia","institution_ids":["https://openalex.org/I165779595"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5087639822"],"corresponding_institution_ids":["https://openalex.org/I165779595"],"apc_list":null,"apc_paid":null,"fwci":0.2486,"has_fulltext":false,"cited_by_count":11,"citation_normalized_percentile":{"value":0.62697092,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":"07","issue":"03","first_page":"317","last_page":"331"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12740","display_name":"Gait Recognition and Analysis","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/T12740","display_name":"Gait Recognition and Analysis","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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9954000115394592,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11227","display_name":"Diabetic Foot Ulcer Assessment and Management","score":0.982200026512146,"subfield":{"id":"https://openalex.org/subfields/2712","display_name":"Endocrinology, Diabetes and Metabolism"},"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/support-vector-machine","display_name":"Support vector machine","score":0.7449777126312256},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7129874229431152},{"id":"https://openalex.org/keywords/gait","display_name":"Gait","score":0.5563294291496277},{"id":"https://openalex.org/keywords/feature-selection","display_name":"Feature selection","score":0.5418804883956909},{"id":"https://openalex.org/keywords/receiver-operating-characteristic","display_name":"Receiver operating characteristic","score":0.5366164445877075},{"id":"https://openalex.org/keywords/cross-validation","display_name":"Cross-validation","score":0.4846287667751312},{"id":"https://openalex.org/keywords/sensitivity","display_name":"Sensitivity (control systems)","score":0.4748409390449524},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4701511859893799},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.46079355478286743},{"id":"https://openalex.org/keywords/population","display_name":"Population","score":0.42608675360679626},{"id":"https://openalex.org/keywords/statistical-model","display_name":"Statistical model","score":0.4135236442089081},{"id":"https://openalex.org/keywords/histogram","display_name":"Histogram","score":0.4112812876701355},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.39204174280166626},{"id":"https://openalex.org/keywords/physical-medicine-and-rehabilitation","display_name":"Physical medicine and rehabilitation","score":0.2159053087234497},{"id":"https://openalex.org/keywords/medicine","display_name":"Medicine","score":0.13111373782157898}],"concepts":[{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.7449777126312256},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7129874229431152},{"id":"https://openalex.org/C151800584","wikidata":"https://www.wikidata.org/wiki/Q2370000","display_name":"Gait","level":2,"score":0.5563294291496277},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.5418804883956909},{"id":"https://openalex.org/C58471807","wikidata":"https://www.wikidata.org/wiki/Q327120","display_name":"Receiver operating characteristic","level":2,"score":0.5366164445877075},{"id":"https://openalex.org/C27181475","wikidata":"https://www.wikidata.org/wiki/Q541014","display_name":"Cross-validation","level":2,"score":0.4846287667751312},{"id":"https://openalex.org/C21200559","wikidata":"https://www.wikidata.org/wiki/Q7451068","display_name":"Sensitivity (control systems)","level":2,"score":0.4748409390449524},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4701511859893799},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.46079355478286743},{"id":"https://openalex.org/C2908647359","wikidata":"https://www.wikidata.org/wiki/Q2625603","display_name":"Population","level":2,"score":0.42608675360679626},{"id":"https://openalex.org/C114289077","wikidata":"https://www.wikidata.org/wiki/Q3284399","display_name":"Statistical model","level":2,"score":0.4135236442089081},{"id":"https://openalex.org/C53533937","wikidata":"https://www.wikidata.org/wiki/Q185020","display_name":"Histogram","level":3,"score":0.4112812876701355},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.39204174280166626},{"id":"https://openalex.org/C99508421","wikidata":"https://www.wikidata.org/wiki/Q2678675","display_name":"Physical medicine and rehabilitation","level":1,"score":0.2159053087234497},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.13111373782157898},{"id":"https://openalex.org/C99454951","wikidata":"https://www.wikidata.org/wiki/Q932068","display_name":"Environmental health","level":1,"score":0.0},{"id":"https://openalex.org/C24326235","wikidata":"https://www.wikidata.org/wiki/Q126095","display_name":"Electronic engineering","level":1,"score":0.0},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.0},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1142/s1469026808002314","is_oa":false,"landing_page_url":"https://doi.org/10.1142/s1469026808002314","pdf_url":null,"source":{"id":"https://openalex.org/S206936884","display_name":"International Journal of Computational Intelligence and Applications","issn_l":"1469-0268","issn":["1469-0268","1757-5885"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310311754","host_organization_name":"Imperial College Press","host_organization_lineage":["https://openalex.org/P4310311754"],"host_organization_lineage_names":["Imperial College Press"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"International Journal of Computational Intelligence and Applications","raw_type":"journal-article"},{"id":"pmh:oai:eprints.vu.edu.au:3776","is_oa":false,"landing_page_url":"https://vuir.vu.edu.au/3776/","pdf_url":null,"source":{"id":"https://openalex.org/S4306400215","display_name":"Victoria University Research Repository (Victoria University)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I41156924","host_organization_name":"Victoria University of Wellington","host_organization_lineage":["https://openalex.org/I41156924"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"PeerReviewed"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/3","display_name":"Good health and well-being","score":0.46000000834465027}],"awards":[],"funders":[{"id":"https://openalex.org/F4320320976","display_name":"Victoria University","ror":"https://ror.org/04j757h98"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":25,"referenced_works":["https://openalex.org/W1560724230","https://openalex.org/W1563088657","https://openalex.org/W1565928583","https://openalex.org/W1604938182","https://openalex.org/W1669104078","https://openalex.org/W1968056506","https://openalex.org/W1976953979","https://openalex.org/W1993839883","https://openalex.org/W2002844845","https://openalex.org/W2027543406","https://openalex.org/W2033299989","https://openalex.org/W2088798815","https://openalex.org/W2092384389","https://openalex.org/W2106452491","https://openalex.org/W2120785797","https://openalex.org/W2130174568","https://openalex.org/W2132600856","https://openalex.org/W2133671386","https://openalex.org/W2156909104","https://openalex.org/W2170242382","https://openalex.org/W2322236858","https://openalex.org/W3137161744","https://openalex.org/W3144619878","https://openalex.org/W4230674625","https://openalex.org/W4233107852"],"related_works":["https://openalex.org/W2107628111","https://openalex.org/W2394004323","https://openalex.org/W2398764543","https://openalex.org/W2027335291","https://openalex.org/W816105089","https://openalex.org/W4210328553","https://openalex.org/W1980417906","https://openalex.org/W1892675750","https://openalex.org/W2007994675","https://openalex.org/W2071206959"],"abstract_inverted_index":{"Trip-related":[0],"falls":[1],"are":[2,289],"a":[3,74,90,147,196,241,280],"major":[4],"problem":[5],"in":[6,12,127,149,154,245,305,308],"the":[7,13,45,56,106,112,124,131,150,159,181,185,205,222,249,309],"elderly":[8,81,84],"population":[9],"and":[10,82,88,114,143,145,174,204,212,231,262,276,291],"research":[11],"area":[14,208],"has":[15,22],"received":[16],"much":[17],"attention":[18],"recently.":[19],"The":[20,36,117,166,187],"focus":[21],"been":[23],"on":[24,50,73,78],"devising":[25],"ways":[26],"of":[27,32,39,47,59,92,172,184,229,251,272,283],"identifying":[28],"individuals":[29],"at":[30],"risk":[31],"sustaining":[33],"such":[34],"falls.":[35,94],"main":[37],"aim":[38],"this":[40,163],"work":[41],"is":[42],"to":[43,105,109,129,157,179,192,225,247,267],"explore":[44],"effectiveness":[46],"models":[48,224],"based":[49],"Support":[51],"Vector":[52],"Machines":[53],"(SVMs)":[54],"for":[55,122,162,297,302],"automated":[57],"recognition":[58],"gait":[60,164,227,306],"patterns":[61,228],"that":[62,257],"exhibit":[63],"falling":[64],"behavior.":[65],"Minimum":[66],"toe":[67],"clearance":[68],"(MTC)":[69],"during":[70],"continuous":[71],"walking":[72],"treadmill":[75],"was":[76,120,190,236,255],"recorded":[77],"10":[79,83,203],"healthy":[80,113,230],"with":[85,89,138,146,199],"balance":[86],"problems":[87],"history":[91],"tripping":[93],"Statistical":[95],"features":[96,218,259],"obtained":[97],"from":[98],"MTC":[99],"histograms":[100],"were":[101,136,176,219,265],"used":[102,178,220],"as":[103],"inputs":[104],"SVM":[107,125,223],"model":[108,126,133,161],"classify":[110],"between":[111],"balance-impaired":[115,232],"subjects.":[116],"leave-one-out":[118],"technique":[119],"utilized":[121],"training":[123],"order":[128,246],"find":[130],"optimal":[132],"parameters.":[134],"Tests":[135],"conducted":[137],"various":[139],"kernels":[140],"(linear,":[141],"Gaussian":[142,197],"polynomial)":[144],"change":[148],"regularization":[151],"parameter,":[152],"C,":[153],"an":[155,269],"effort":[156],"identify":[158],"optimum":[160],"data.":[165],"receiver":[167],"operating":[168],"characteristic":[169],"(ROC)":[170],"plots":[171],"sensitivity":[173,211,275],"specificity":[175],"further":[177,237],"evaluate":[180],"diagnostic":[182,298],"performance":[183],"model.":[186],"maximum":[188,206,263],"accuracy":[189,235,271],"found":[191,256],"be":[193,293],"90%":[194],"using":[195,240,279],"kernel":[198,282],"\u03c3":[200],"2":[201],"=":[202],"ROC":[207],"0.98":[209],"(80%":[210],"100%":[213,277],"specificity),":[214],"when":[215],"all":[216],"statistical":[217],"by":[221,239],"diagnose":[226],"individuals.":[233],"This":[234],"improved":[238,270],"feature":[242],"selection":[243],"method":[244],"reduce":[248],"effect":[250],"redundant":[252],"features.":[253],"It":[254],"two":[258],"(standard":[260],"deviation":[261],"value)":[264],"adequate":[266],"give":[268],"95%":[273],"(90%":[274],"specificity)":[278],"polynomial":[281],"degree":[284],"2.":[285],"These":[286],"preliminary":[287],"results":[288],"encouraging":[290],"could":[292],"useful":[294],"not":[295],"only":[296],"applications":[299],"but":[300],"also":[301],"evaluating":[303],"improvements":[304],"function":[307],"clinical/rehabilitation":[310],"contexts.":[311]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2021,"cited_by_count":3},{"year":2020,"cited_by_count":1},{"year":2016,"cited_by_count":2},{"year":2015,"cited_by_count":2},{"year":2012,"cited_by_count":1}],"updated_date":"2026-03-20T23:20:44.827607","created_date":"2025-10-10T00:00:00"}
