{"id":"https://openalex.org/W3001703599","doi":"https://doi.org/10.1145/3372454.3372471","title":"Statistical Comparison of 3D Scanning and Manual Measurement Methods in Anthropometry","display_name":"Statistical Comparison of 3D Scanning and Manual Measurement Methods in Anthropometry","publication_year":2019,"publication_date":"2019-11-20","ids":{"openalex":"https://openalex.org/W3001703599","doi":"https://doi.org/10.1145/3372454.3372471","mag":"3001703599"},"language":"en","primary_location":{"id":"doi:10.1145/3372454.3372471","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3372454.3372471","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2019 3rd International Conference on Big Data Research","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/A5031699156","display_name":"Jing Zhao","orcid":"https://orcid.org/0000-0001-6192-0335"},"institutions":[{"id":"https://openalex.org/I4210146077","display_name":"China National Institute of Standardization","ror":"https://ror.org/03qzxj964","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210146077"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Jing Zhao","raw_affiliation_strings":["China National Institute of Standardization, Beijing, China"],"affiliations":[{"raw_affiliation_string":"China National Institute of Standardization, Beijing, China","institution_ids":["https://openalex.org/I4210146077"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100403428","display_name":"Fan Zhang","orcid":"https://orcid.org/0000-0002-0343-3499"},"institutions":[{"id":"https://openalex.org/I4210146077","display_name":"China National Institute of Standardization","ror":"https://ror.org/03qzxj964","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210146077"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Fan Zhang","raw_affiliation_strings":["China National Institute of Standardization, Beijing, China"],"affiliations":[{"raw_affiliation_string":"China National Institute of Standardization, Beijing, China","institution_ids":["https://openalex.org/I4210146077"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5091788670","display_name":"Wenxing Ding","orcid":null},"institutions":[{"id":"https://openalex.org/I4210146077","display_name":"China National Institute of Standardization","ror":"https://ror.org/03qzxj964","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210146077"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wenxing Ding","raw_affiliation_strings":["China National Institute of Standardization, Beijing, China"],"affiliations":[{"raw_affiliation_string":"China National Institute of Standardization, Beijing, China","institution_ids":["https://openalex.org/I4210146077"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5031699156"],"corresponding_institution_ids":["https://openalex.org/I4210146077"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.19500931,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"72","last_page":"75"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10719","display_name":"3D Shape Modeling and Analysis","score":0.9496999979019165,"subfield":{"id":"https://openalex.org/subfields/2206","display_name":"Computational Mechanics"},"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/T10719","display_name":"3D Shape Modeling and Analysis","score":0.9496999979019165,"subfield":{"id":"https://openalex.org/subfields/2206","display_name":"Computational Mechanics"},"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/T11595","display_name":"Textile materials and evaluations","score":0.9409000277519226,"subfield":{"id":"https://openalex.org/subfields/2507","display_name":"Polymers and Plastics"},"field":{"id":"https://openalex.org/fields/25","display_name":"Materials Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12006","display_name":"Ergonomics and Musculoskeletal Disorders","score":0.9014999866485596,"subfield":{"id":"https://openalex.org/subfields/3207","display_name":"Social Psychology"},"field":{"id":"https://openalex.org/fields/32","display_name":"Psychology"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/heel","display_name":"Heel","score":0.7221358418464661},{"id":"https://openalex.org/keywords/outlier","display_name":"Outlier","score":0.7131591439247131},{"id":"https://openalex.org/keywords/calipers","display_name":"Calipers","score":0.5200459957122803},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5179859399795532},{"id":"https://openalex.org/keywords/normality","display_name":"Normality","score":0.5094693303108215},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4157281517982483},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.3641813397407532},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.3518967926502228},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.33374449610710144},{"id":"https://openalex.org/keywords/medicine","display_name":"Medicine","score":0.2497996985912323}],"concepts":[{"id":"https://openalex.org/C2777248721","wikidata":"https://www.wikidata.org/wiki/Q174647","display_name":"Heel","level":2,"score":0.7221358418464661},{"id":"https://openalex.org/C79337645","wikidata":"https://www.wikidata.org/wiki/Q779824","display_name":"Outlier","level":2,"score":0.7131591439247131},{"id":"https://openalex.org/C186738849","wikidata":"https://www.wikidata.org/wiki/Q12408","display_name":"Calipers","level":2,"score":0.5200459957122803},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5179859399795532},{"id":"https://openalex.org/C2776157432","wikidata":"https://www.wikidata.org/wiki/Q1375683","display_name":"Normality","level":2,"score":0.5094693303108215},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4157281517982483},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.3641813397407532},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3518967926502228},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.33374449610710144},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.2497996985912323},{"id":"https://openalex.org/C105702510","wikidata":"https://www.wikidata.org/wiki/Q514","display_name":"Anatomy","level":1,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3372454.3372471","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3372454.3372471","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2019 3rd International Conference on Big Data Research","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W2371094352","https://openalex.org/W2384705931","https://openalex.org/W2362544213","https://openalex.org/W1987081680","https://openalex.org/W2353448705","https://openalex.org/W3195805269","https://openalex.org/W2378648583","https://openalex.org/W2092110463","https://openalex.org/W2381783751","https://openalex.org/W3193432347"],"abstract_inverted_index":{"3D":[0,159,187,207],"scanner":[1],"and":[2,24,58,70,85,136,145,153,171,186],"Martin":[3],"ruler":[4],"are":[5,35,62,67,115,121,147,166],"two":[6,18],"common":[7],"measurement":[8,19,51,143,185],"methods":[9,20],"to":[10,37,108,117],"perform":[11],"anthropometric":[12],"measurements.":[13],"This":[14],"paper":[15],"compare":[16],"the":[17,42,49,53,65,82,86,95,98,101,105,110,133,137,141,150,154,158,202,206],"for":[21,81,93,201],"chest":[22,56,83,134,151,169,191],"width":[23,26,57,60,84,88,135,139,152,156,170,192],"heel":[25,59,87,138,155,172,194],"by":[27],"statistical":[28,33],"method.":[29,161],"Firstly,":[30],"necessary":[31],"descriptive":[32],"analysis":[34],"performed":[36,167],"obtain":[38],"general":[39],"knowledge":[40],"of":[41,55,190,205],"data.":[43],"The":[44,123,196],"results":[45,124],"show":[46,125],"that":[47,126,177],"using":[48],"manual":[50,142,184],"method,":[52,144],"means":[54],"both":[61],"relatively":[63],"small,":[64],"data":[66,99],"more":[68],"concentrated,":[69],"there":[71,120,127,146,178],"is":[72,128,175,179],"no":[73,129],"abnormal":[74,130],"point.":[75],"Additionally,":[76],"we":[77,103],"conduct":[78],"normality":[79],"test":[80,107],"then":[89],"select":[90],"appropriate":[91],"method":[92],"detecting":[94],"outliers.":[96,122],"If":[97],"follows":[100],"normality,":[102],"use":[104,204],"Grubbs":[106],"detect":[109],"outliers;":[111],"if":[112],"not,":[113],"boxplots":[114],"used":[116],"determine":[118],"whether":[119],"point":[131],"in":[132,149],"under":[140,157],"outliers":[148],"scanning":[160],"Finally,":[162],"a":[163,180,199],"two-sample":[164],"T-test":[165],"on":[168],"width.":[173,195],"It":[174],"found":[176],"significant":[181],"difference":[182],"between":[183],"scanning,":[188],"regardless":[189],"or":[193],"result":[197],"provides":[198],"basis":[200],"correct":[203],"scanner.":[208]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}