{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,24]],"date-time":"2025-12-24T12:16:30Z","timestamp":1766578590734,"version":"build-2065373602"},"reference-count":19,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2019,2,21]],"date-time":"2019-02-21T00:00:00Z","timestamp":1550707200000},"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>In order to gain insight into skiing performance, it is necessary to determine the point where each turn begins. Recent developments in sensor technology have made it possible to develop simpler automatic turn detection methodologies, however they are not feasible for regular use. The aim of this study was to develop a sensor set up and an algorithm to precisely detect turns during alpine ski, which is feasible for a daily use. An IMU was attached to the posterior upper cuff of each ski boot. Turn movements were reproduced on a ski-ergometer at different turn durations and slopes. Algorithms were developed to analyze vertical, medio-lateral, anterior-posterior axes, and resultant accelerometer and gyroscope signals. Raw signals, and signals filtered with 3, 6, 9, and 12 Hz cut-offs were used to identify turn switch points. Video recordings were assessed to establish a reference turn-switch and precision (mean bias = 5.2, LoA = 51.4 ms). Precision was adjusted based on reference and the best signals were selected. The z-axis and resultant gyroscope signals, filtered at 3Hz are the most precise signals (0.056 and 0.063 s, respectively) to automatically detect turn switches during alpine skiing using this simple system.<\/jats:p>","DOI":"10.3390\/s19040902","type":"journal-article","created":{"date-parts":[[2019,2,22]],"date-time":"2019-02-22T03:49:44Z","timestamp":1550807384000},"page":"902","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":35,"title":["Development of an Automatic Alpine Skiing Turn Detection Algorithm Based on a Simple Sensor Setup"],"prefix":"10.3390","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6974-6893","authenticated-orcid":false,"given":"Aaron","family":"Mart\u00ednez","sequence":"first","affiliation":[{"name":"Department of Sport and Exercise Science, University of Salzburg, Schlossallee 49, 5400 Hallein\/Rif, Austria"}]},{"given":"R\u00fcdiger","family":"Jahnel","sequence":"additional","affiliation":[{"name":"Department of Sport and Exercise Science, University of Salzburg, Schlossallee 49, 5400 Hallein\/Rif, Austria"}]},{"given":"Michael","family":"Buchecker","sequence":"additional","affiliation":[{"name":"Department of Sport and Exercise Science, University of Salzburg, Schlossallee 49, 5400 Hallein\/Rif, Austria"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2731-4662","authenticated-orcid":false,"given":"Cory","family":"Snyder","sequence":"additional","affiliation":[{"name":"Department of Sport and Exercise Science, University of Salzburg, Schlossallee 49, 5400 Hallein\/Rif, Austria"}]},{"given":"Richard","family":"Brunauer","sequence":"additional","affiliation":[{"name":"Salzburg Research Forschungsgesellschaft m.b.H., Techno-Z III, Jakob-Haringer-Stra\u00dfe 5, 5020 Salzburg, Austria"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6685-1540","authenticated-orcid":false,"given":"Thomas","family":"St\u00f6ggl","sequence":"additional","affiliation":[{"name":"Department of Sport and Exercise Science, University of Salzburg, Schlossallee 49, 5400 Hallein\/Rif, Austria"}]}],"member":"1968","published-online":{"date-parts":[[2019,2,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"e38","DOI":"10.1111\/sms.12007","article-title":"Aerodynamic drag is not the major determinant of performance during giant slalom skiing at the elite level","volume":"23","author":"Supej","year":"2013","journal-title":"Scand. 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