{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,8]],"date-time":"2026-07-08T11:15:00Z","timestamp":1783509300521,"version":"3.55.0"},"reference-count":31,"publisher":"MDPI AG","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,3,11]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    <jats:bold>Background:<\/jats:bold>\n                    It\u2019s important to determine the plantar pressure distribution of school children by applying static and dynamic foot analyses using a pedobarography device. However, it\u2019s difficult to obtain clear interpretations from results which can be explained by a large number of plantar pressure variables. The aim of this study is to use Principal Component Analysis (PCA) to predict main components for reducing the size of big data sets, provide a practical overview and minimize information loss on the subject of plantar pressure assessment in youths.\n                  <\/jats:p>\n                  <jats:p>\n                    <jats:bold>Methods:<\/jats:bold>\n                    In total, 112 school children were included in the current study (average age 10.58 \u00b1 1.27 years, body mass index 18.86 \u00b1 4.33 kg \/ m\n                    <jats:sup>2<\/jats:sup>\n                    ). During the research, a Sensor Medica Freemed pedobarography device was used to obtain plantar pressure data. Each foot was divided into six anatomical regions and evaluated. Global and regional plantar pressure distribution, load and surface areas, pressure time integrals, weight ratios and geometric foot properties were calculated.\n                  <\/jats:p>\n                  <jats:p>\n                    <jats:bold>Results:<\/jats:bold>\n                    PCA yielded ten principal component (PC) that together account for 81.88% of the variation in the data set and represent new and distinct patterns. Thus, 137 variables affecting the subject were reduced to ten components.\n                  <\/jats:p>\n                  <jats:p>\n                    <jats:bold>Conclusions:<\/jats:bold>\n                    Static and dynamic plantar pressure distribution, which is affected by many variables, can be reduced to ten components by PCA, making the research results more concise and understandable.\n                  <\/jats:p>","DOI":"10.7547\/21-048","type":"journal-article","created":{"date-parts":[[2022,3,16]],"date-time":"2022-03-16T15:50:23Z","timestamp":1647445823000},"page":"1-30","source":"Crossref","is-referenced-by-count":2,"title":["New Distinct Component Patterns for Plantar Pressure Variables by Using Principal Component Analysis"],"prefix":"10.3390","author":[{"given":"Ba\u015far","family":"\u00d6zt\u00fcrk","sequence":"first","affiliation":[{"name":"Biruni University Faculty of Health Sciences Physiotherapy and Rehabilitation Department, Topkapi, Istanbul."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yusuf","family":"\u00c7elik","sequence":"additional","affiliation":[{"name":"Biruni University Medical Faculty Department of Biostatistics and Medical Informatics, Topkapi, Istanbul."}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","reference":[{"key":"B1","series-title":"J Biomech","doi-asserted-by":"publisher","first-page":"19","DOI":"10.1016\/j.jbiomech.2012.09.007","article-title":"Clustering and classification of regional peak plantar pressures of diabetic feet","volume":"46","author":"Bennetts CJ","year":"2013","unstructured":"Bennetts CJ, Owings TM, Erdemir A, et al: Clustering and classification of regional peak plantar pressures of diabetic feet. 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