{"id":"https://openalex.org/W2753285598","doi":"https://doi.org/10.1109/icme.2017.8019410","title":"Weakly structured information aggregation for upper-body posture assessment using ConvNets","display_name":"Weakly structured information aggregation for upper-body posture assessment using ConvNets","publication_year":2017,"publication_date":"2017-07-01","ids":{"openalex":"https://openalex.org/W2753285598","doi":"https://doi.org/10.1109/icme.2017.8019410","mag":"2753285598"},"language":"en","primary_location":{"id":"doi:10.1109/icme.2017.8019410","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icme.2017.8019410","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE International Conference on Multimedia and Expo (ICME)","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/A5057351806","display_name":"Zewei Ding","orcid":"https://orcid.org/0000-0002-5220-1686"},"institutions":[{"id":"https://openalex.org/I204824540","display_name":"University of Wollongong","ror":"https://ror.org/00jtmb277","country_code":"AU","type":"education","lineage":["https://openalex.org/I204824540"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Zewei Ding","raw_affiliation_strings":["Advanced Multimedia Research Lab, University of Wollongong, Australia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Advanced Multimedia Research Lab, University of Wollongong, Australia","institution_ids":["https://openalex.org/I204824540"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100695040","display_name":"Wanqing Li","orcid":"https://orcid.org/0000-0002-4427-2687"},"institutions":[{"id":"https://openalex.org/I204824540","display_name":"University of Wollongong","ror":"https://ror.org/00jtmb277","country_code":"AU","type":"education","lineage":["https://openalex.org/I204824540"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Wanqing Li","raw_affiliation_strings":["Advanced Multimedia Research Lab, University of Wollongong, Australia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Advanced Multimedia Research Lab, University of Wollongong, Australia","institution_ids":["https://openalex.org/I204824540"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5042680345","display_name":"Pichao Wang","orcid":"https://orcid.org/0000-0002-1430-0237"},"institutions":[{"id":"https://openalex.org/I204824540","display_name":"University of Wollongong","ror":"https://ror.org/00jtmb277","country_code":"AU","type":"education","lineage":["https://openalex.org/I204824540"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Pichao Wang","raw_affiliation_strings":["Advanced Multimedia Research Lab, University of Wollongong, Australia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Advanced Multimedia Research Lab, University of Wollongong, Australia","institution_ids":["https://openalex.org/I204824540"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5081319088","display_name":"Philip Ogunbona","orcid":"https://orcid.org/0000-0003-4119-2873"},"institutions":[{"id":"https://openalex.org/I204824540","display_name":"University of Wollongong","ror":"https://ror.org/00jtmb277","country_code":"AU","type":"education","lineage":["https://openalex.org/I204824540"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Philip Ogunbona","raw_affiliation_strings":["Advanced Multimedia Research Lab, University of Wollongong, Australia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Advanced Multimedia Research Lab, University of Wollongong, Australia","institution_ids":["https://openalex.org/I204824540"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5090953133","display_name":"Ling Qin","orcid":"https://orcid.org/0000-0003-4797-6476"},"institutions":[{"id":"https://openalex.org/I4210115919","display_name":"The People's Hospital of Guangxi Zhuang Autonomous Region","ror":"https://ror.org/02aa8kj12","country_code":"CN","type":"healthcare","lineage":["https://openalex.org/I4210115919"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ling Qin","raw_affiliation_strings":["The People's Hospital of Guangxi zhuang Autonomous Region, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"The People's Hospital of Guangxi zhuang Autonomous Region, China","institution_ids":["https://openalex.org/I4210115919"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":5,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.12339727,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"1512","last_page":"1517"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11398","display_name":"Hand Gesture Recognition Systems","score":0.9923999905586243,"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"}},"topics":[{"id":"https://openalex.org/T11398","display_name":"Hand Gesture Recognition Systems","score":0.9923999905586243,"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"}},{"id":"https://openalex.org/T12994","display_name":"Infrared Thermography in Medicine","score":0.9918000102043152,"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/T10812","display_name":"Human Pose and Action Recognition","score":0.9783999919891357,"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/torso","display_name":"Torso","score":0.8202508687973022},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7185652852058411},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6651626825332642},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6269021034240723},{"id":"https://openalex.org/keywords/upper-body","display_name":"Upper body","score":0.5486012101173401},{"id":"https://openalex.org/keywords/body-posture","display_name":"Body posture","score":0.5173161029815674},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.47693532705307007},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.47473010420799255},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4372097849845886},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.37946617603302},{"id":"https://openalex.org/keywords/physical-medicine-and-rehabilitation","display_name":"Physical medicine and rehabilitation","score":0.13175207376480103}],"concepts":[{"id":"https://openalex.org/C523889960","wikidata":"https://www.wikidata.org/wiki/Q160695","display_name":"Torso","level":2,"score":0.8202508687973022},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7185652852058411},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6651626825332642},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6269021034240723},{"id":"https://openalex.org/C2992583082","wikidata":"https://www.wikidata.org/wiki/Q9645","display_name":"Upper body","level":3,"score":0.5486012101173401},{"id":"https://openalex.org/C2987268071","wikidata":"https://www.wikidata.org/wiki/Q29034095","display_name":"Body posture","level":2,"score":0.5173161029815674},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.47693532705307007},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.47473010420799255},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4372097849845886},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.37946617603302},{"id":"https://openalex.org/C99508421","wikidata":"https://www.wikidata.org/wiki/Q2678675","display_name":"Physical medicine and rehabilitation","level":1,"score":0.13175207376480103},{"id":"https://openalex.org/C105702510","wikidata":"https://www.wikidata.org/wiki/Q514","display_name":"Anatomy","level":1,"score":0.0},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.0},{"id":"https://openalex.org/C153396756","wikidata":"https://www.wikidata.org/wiki/Q1785966","display_name":"Physical strength","level":2,"score":0.0},{"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}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/icme.2017.8019410","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icme.2017.8019410","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE International Conference on Multimedia and Expo (ICME)","raw_type":"proceedings-article"},{"id":"pmh:oai:ro.uow.edu.au:eispapers1-1715","is_oa":false,"landing_page_url":"https://ro.uow.edu.au/eispapers1/714","pdf_url":null,"source":{"id":"https://openalex.org/S4306400510","display_name":"Research Online (University of Wollongong)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I204824540","host_organization_name":"University of Wollongong","host_organization_lineage":["https://openalex.org/I204824540"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Faculty of Engineering and Information Sciences - Papers: Part B","raw_type":"presentation"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"No poverty","score":0.6899999976158142,"id":"https://metadata.un.org/sdg/1"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":28,"referenced_works":["https://openalex.org/W219040644","https://openalex.org/W1850888726","https://openalex.org/W1909903157","https://openalex.org/W1925417509","https://openalex.org/W1948369226","https://openalex.org/W1980172175","https://openalex.org/W1991527267","https://openalex.org/W2072692231","https://openalex.org/W2077193682","https://openalex.org/W2096733369","https://openalex.org/W2113325037","https://openalex.org/W2139010248","https://openalex.org/W2151103935","https://openalex.org/W2155893237","https://openalex.org/W2158268505","https://openalex.org/W2163605009","https://openalex.org/W2168407388","https://openalex.org/W2330154883","https://openalex.org/W2344034899","https://openalex.org/W2428123325","https://openalex.org/W2526041356","https://openalex.org/W2591961134","https://openalex.org/W2950094539","https://openalex.org/W3099206234","https://openalex.org/W3103919331","https://openalex.org/W6640740087","https://openalex.org/W6684191040","https://openalex.org/W6734693744"],"related_works":["https://openalex.org/W2626264590","https://openalex.org/W2519201755","https://openalex.org/W280667413","https://openalex.org/W2278894726","https://openalex.org/W1883006668","https://openalex.org/W2081636342","https://openalex.org/W2921252167","https://openalex.org/W2753894412","https://openalex.org/W2682143310","https://openalex.org/W2055247639"],"abstract_inverted_index":{"Posture":[0],"assessment":[1,19,82],"aims":[2],"to":[3,26,43,58],"determine":[4,59],"the":[5,60,104,107],"risk":[6,92],"associated":[7],"with":[8,68,86],"poor":[9],"posture":[10,18,91,100],"and":[11,51,75,81,110],"thus":[12],"avoid":[13],"injury":[14],"in":[15],"subjects.":[16],"Upper-body":[17],"from":[20,47],"images":[21],"offers":[22],"an":[23],"attractive":[24],"alternative":[25],"manual":[27],"methods":[28],"by":[29],"directly":[30],"extracting":[31],"relevant":[32],"features":[33,46,54,65],"for":[34],"classification.":[35],"A":[36],"deep":[37],"convolutional":[38],"neural":[39],"network":[40,111],"is":[41],"proposed":[42,108],"extract":[44],"structured":[45,64],"different":[48],"body":[49],"parts":[50],"learn":[52],"shared":[53,79],"that":[55],"are":[56,66,84],"used":[57],"appropriate":[61],"assessment.":[62],"The":[63,78],"learned":[67,85],"triplet-based":[69],"rank":[70],"constraints":[71,88],"based":[72,89],"on":[73,90,96],"head":[74],"torso":[76],"separately.":[77],"feature":[80],"function":[83],"soft-max":[87],"measurements.":[93],"Experimental":[94],"evaluation":[95],"a":[97],"self-collected":[98],"upper-body":[99],"dataset":[101],"has":[102],"verified":[103],"efficacy":[105],"of":[106],"method":[109],"architecture.":[112]},"counts_by_year":[{"year":2022,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}