{"id":"https://openalex.org/W2035491978","doi":"https://doi.org/10.1109/gcce.2013.6664875","title":"Adaptive gait recognition model and automatic velocity-robust feature selection based on a new Constrained Expectation Conditional-Maximization learning algorithm","display_name":"Adaptive gait recognition model and automatic velocity-robust feature selection based on a new Constrained Expectation Conditional-Maximization learning algorithm","publication_year":2013,"publication_date":"2013-10-01","ids":{"openalex":"https://openalex.org/W2035491978","doi":"https://doi.org/10.1109/gcce.2013.6664875","mag":"2035491978"},"language":"en","primary_location":{"id":"doi:10.1109/gcce.2013.6664875","is_oa":false,"landing_page_url":"https://doi.org/10.1109/gcce.2013.6664875","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2013 IEEE 2nd Global Conference on Consumer Electronics (GCCE)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5115601773","display_name":"Dapeng Zhang","orcid":"https://orcid.org/0000-0001-7349-8709"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Dapeng Zhang","raw_affiliation_strings":["RIKEN-TRI Collaboration Center, Nagoya, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"RIKEN-TRI Collaboration Center, Nagoya, Japan","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5088437154","display_name":"Ryojun Ikeura","orcid":"https://orcid.org/0000-0001-9174-4379"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ryojun Ikeura","raw_affiliation_strings":["RIKEN-TRI Collaboration Center, Nagoya, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"RIKEN-TRI Collaboration Center, Nagoya, Japan","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5075659554","display_name":"Shinkichi Inagaki","orcid":"https://orcid.org/0000-0003-0850-0854"},"institutions":[{"id":"https://openalex.org/I60134161","display_name":"Nagoya University","ror":"https://ror.org/04chrp450","country_code":"JP","type":"education","lineage":["https://openalex.org/I60134161"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Shinkichi Inagaki","raw_affiliation_strings":["Dept. of Mech. Science & Engineering, Nagoya University, Nagoya, Japan","Dept. of Mech. Sci. & Eng., Nagoya Univ., Nagoya, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Dept. of Mech. Science & Engineering, Nagoya University, Nagoya, Japan","institution_ids":["https://openalex.org/I60134161"]},{"raw_affiliation_string":"Dept. of Mech. Sci. & Eng., Nagoya Univ., Nagoya, Japan","institution_ids":["https://openalex.org/I60134161"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5079754847","display_name":"Tatsuya Suzuki","orcid":"https://orcid.org/0000-0002-0182-308X"},"institutions":[{"id":"https://openalex.org/I60134161","display_name":"Nagoya University","ror":"https://ror.org/04chrp450","country_code":"JP","type":"education","lineage":["https://openalex.org/I60134161"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Tatsuya Suzuki","raw_affiliation_strings":["Dept. of Mech. Science & Engineering, Nagoya University, Nagoya, Japan","Dept. of Mech. Sci. & Eng., Nagoya Univ., Nagoya, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Dept. of Mech. Science & Engineering, Nagoya University, Nagoya, Japan","institution_ids":["https://openalex.org/I60134161"]},{"raw_affiliation_string":"Dept. of Mech. Sci. & Eng., Nagoya Univ., Nagoya, Japan","institution_ids":["https://openalex.org/I60134161"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"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":"1","issue":null,"first_page":"417","last_page":"421"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12740","display_name":"Gait Recognition and Analysis","score":0.9998999834060669,"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":0.9998999834060669,"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/T10812","display_name":"Human Pose and Action Recognition","score":0.9947999715805054,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.9901000261306763,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/expectation\u2013maximization-algorithm","display_name":"Expectation\u2013maximization algorithm","score":0.6574596166610718},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.621167778968811},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5606400370597839},{"id":"https://openalex.org/keywords/estimator","display_name":"Estimator","score":0.4985518455505371},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4965665936470032},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.45674529671669006},{"id":"https://openalex.org/keywords/hidden-markov-model","display_name":"Hidden Markov model","score":0.4482076168060303},{"id":"https://openalex.org/keywords/marginal-likelihood","display_name":"Marginal likelihood","score":0.4432523250579834},{"id":"https://openalex.org/keywords/conditional-probability-distribution","display_name":"Conditional probability distribution","score":0.4408068060874939},{"id":"https://openalex.org/keywords/bayesian-information-criterion","display_name":"Bayesian information criterion","score":0.4336020052433014},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.41505885124206543},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.4086788594722748},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3705838918685913},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.2471400797367096},{"id":"https://openalex.org/keywords/maximum-likelihood","display_name":"Maximum likelihood","score":0.2391815185546875}],"concepts":[{"id":"https://openalex.org/C182081679","wikidata":"https://www.wikidata.org/wiki/Q1275153","display_name":"Expectation\u2013maximization algorithm","level":3,"score":0.6574596166610718},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.621167778968811},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5606400370597839},{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.4985518455505371},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4965665936470032},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.45674529671669006},{"id":"https://openalex.org/C23224414","wikidata":"https://www.wikidata.org/wiki/Q176769","display_name":"Hidden Markov model","level":2,"score":0.4482076168060303},{"id":"https://openalex.org/C95923904","wikidata":"https://www.wikidata.org/wiki/Q6760420","display_name":"Marginal likelihood","level":3,"score":0.4432523250579834},{"id":"https://openalex.org/C43555835","wikidata":"https://www.wikidata.org/wiki/Q2300258","display_name":"Conditional probability distribution","level":2,"score":0.4408068060874939},{"id":"https://openalex.org/C168136583","wikidata":"https://www.wikidata.org/wiki/Q1988242","display_name":"Bayesian information criterion","level":2,"score":0.4336020052433014},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.41505885124206543},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.4086788594722748},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3705838918685913},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2471400797367096},{"id":"https://openalex.org/C49781872","wikidata":"https://www.wikidata.org/wiki/Q1045555","display_name":"Maximum likelihood","level":2,"score":0.2391815185546875},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/gcce.2013.6664875","is_oa":false,"landing_page_url":"https://doi.org/10.1109/gcce.2013.6664875","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2013 IEEE 2nd Global Conference on Consumer Electronics (GCCE)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":6,"referenced_works":["https://openalex.org/W1967639437","https://openalex.org/W2118070810","https://openalex.org/W2129887056","https://openalex.org/W2133600694","https://openalex.org/W2138350282","https://openalex.org/W2142532896"],"related_works":["https://openalex.org/W2140773153","https://openalex.org/W2176075850","https://openalex.org/W3124166636","https://openalex.org/W2970192821","https://openalex.org/W3019472554","https://openalex.org/W2921881736","https://openalex.org/W2115621436","https://openalex.org/W2963391287","https://openalex.org/W4320931070","https://openalex.org/W2949522784"],"abstract_inverted_index":{"A":[0],"robust":[1],"and":[2,48,96],"compact":[3],"human":[4,46,52],"motion":[5,47],"model":[6,53,84,146],"is":[7,80,101,110,132],"desirable":[8],"in":[9,29,178],"many":[10],"security":[11],"applications":[12],"from":[13,23,76],"public":[14],"facilities":[15],"to":[16,40,49,55,148,184],"personal":[17],"devices.":[18],"Shape":[19],"features":[20,44,120],"are":[21,36,121,124],"extracted":[22],"the":[24,51,56,97,104,113,118,129,136,144,162],"perspective":[25],"of":[26,34,45,90,93],"computer":[27],"vision":[28],"most":[30,33],"researches.":[31],"However,":[32],"them":[35],"application-dependent.":[37],"In":[38],"order":[39],"explore":[41],"more":[42],"dynamical":[43],"make":[50],"adaptable":[54],"varying":[57],"environments,":[58],"a":[59,86,149,158,179],"new":[60],"Stochastic":[61],"Switched":[62],"Auto-Regressive":[63],"Model":[64],"together":[65],"with":[66,165,182],"an":[67],"innovative":[68],"Constrained":[69],"Expectation":[70],"Conditional-Maximization":[71],"algorithm":[72,100,139],"which":[73],"utilizes":[74],"pre-knowledge":[75],"feature":[77],"space":[78],"analysis":[79],"proposed.":[81],"The":[82,108,153,168],"proposed":[83,102,154],"has":[85],"circular":[87],"topology":[88],"consisted":[89],"2":[91],"pairs":[92],"correlated":[94],"states":[95],"constrained":[98],"ECM":[99],"under":[103],"model's":[105],"unique":[106],"structure.":[107],"problem":[109],"complicated":[111],"by":[112],"fact":[114],"that,":[115],"even":[116],"though":[117],"dominant":[119],"dynamic,":[122],"there":[123],"significant":[125],"static":[126],"features.":[127],"Modeling":[128],"underlying":[130],"behavior":[131],"challenging":[133],"especially":[134],"when":[135],"parameter":[137],"estimation":[138],"does":[140],"not":[141],"guarantee":[142],"that":[143],"updated":[145],"converges":[147],"maximum":[150,176],"likelihood":[151,177],"estimator.":[152],"method":[155,170],"can":[156,171],"produce":[157],"probability":[159],"distribution":[160],"over":[161],"latent":[163],"variables":[164],"point":[166],"estimates.":[167],"modeling":[169],"be":[172],"reviewed":[173],"as":[174],"approximating":[175],"non-Bayesian":[180],"way":[181],"adaptability":[183],"changing":[185],"walking":[186],"velocity.":[187]},"counts_by_year":[],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}