{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,25]],"date-time":"2026-07-25T16:03:17Z","timestamp":1784995397024,"version":"3.55.0"},"reference-count":20,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2018,1,9]],"date-time":"2018-01-09T00:00:00Z","timestamp":1515456000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Radio frequency (RF)-based indoor positioning systems (IPSs) use wireless technologies (including Wi-Fi, Zigbee, Bluetooth, and ultra-wide band (UWB)) to estimate the location of persons in areas where no Global Positioning System (GPS) reception is available, for example in indoor stadiums or sports halls. Of the above-mentioned forms of radio frequency (RF) technology, UWB is considered one of the most accurate approaches because it can provide positioning estimates with centimeter-level accuracy. However, it is not yet known whether UWB can also offer such accurate position estimates during strenuous dynamic activities in which moves are characterized by fast changes in direction and velocity. To answer this question, this paper investigates the capabilities of UWB indoor localization systems for tracking athletes during their complex (and most of the time unpredictable) movements. To this end, we analyze the impact of on-body tag placement locations and human movement patterns on localization accuracy and communication reliability. Moreover, two localization algorithms (particle filter and Kalman filter) with different optimizations (bias removal, non-line-of-sight (NLoS) detection, and path determination) are implemented. It is shown that although the optimal choice of optimization depends on the type of movement patterns, some of the improvements can reduce the localization error by up to 31%. Overall, depending on the selected optimization and on-body tag placement, our algorithms show good results in terms of positioning accuracy, with average errors in position estimates of 20 cm. This makes UWB a suitable approach for tracking dynamic athletic activities.<\/jats:p>","DOI":"10.3390\/s18010168","type":"journal-article","created":{"date-parts":[[2018,1,9]],"date-time":"2018-01-09T14:00:08Z","timestamp":1515506408000},"page":"168","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":106,"title":["Experimental Evaluation of UWB Indoor Positioning for Sport Postures"],"prefix":"10.3390","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6857-2019","authenticated-orcid":false,"given":"Matteo","family":"Ridolfi","sequence":"first","affiliation":[{"name":"imec, IDLab, Department of Information Technology, Ghent University, 9000 Gent, Belgium"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Stef","family":"Vandermeeren","sequence":"additional","affiliation":[{"name":"Department of Telecommunications and Information Processing, Ghent University, 9000 Gent, Belgium"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jense","family":"Defraye","sequence":"additional","affiliation":[{"name":"imec, IDLab, Department of Information Technology, Ghent University, 9000 Gent, Belgium"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0667-3276","authenticated-orcid":false,"given":"Heidi","family":"Steendam","sequence":"additional","affiliation":[{"name":"Department of Telecommunications and Information Processing, Ghent University, 9000 Gent, Belgium"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Joeri","family":"Gerlo","sequence":"additional","affiliation":[{"name":"Department of Movement and Sports Sciences, Ghent University, 9000 Gent, Belgium"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dirk","family":"De Clercq","sequence":"additional","affiliation":[{"name":"Department of Movement and Sports Sciences, Ghent University, 9000 Gent, Belgium"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2039-007X","authenticated-orcid":false,"given":"Jeroen","family":"Hoebeke","sequence":"additional","affiliation":[{"name":"imec, IDLab, Department of Information Technology, Ghent University, 9000 Gent, Belgium"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0214-5751","authenticated-orcid":false,"given":"Eli","family":"De Poorter","sequence":"additional","affiliation":[{"name":"imec, IDLab, Department of Information Technology, Ghent University, 9000 Gent, Belgium"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2018,1,9]]},"reference":[{"key":"ref_1","unstructured":"(2017, September 10). 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