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Automatic classification of the Sub-Techniques (Gears) used in cross-country ski skating employing a mobile phone
University of Salzburg, Austria; Mid Sweden University, Sweden.
RISE, Swedish ICT, SICS. KTH Royal Institute of Technology, Sweden.ORCID iD: 0000-0001-8577-6745
RISE, Swedish ICT, SICS.ORCID iD: 0000-0002-3639-9637
Mid Sweden University, Sweden.
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2014 (English)In: Sensors, E-ISSN 1424-8220, Vol. 14, no 11, p. 20589-20601Article in journal (Refereed) Published
Abstract [en]

The purpose of the current study was to develop and validate an automatic algorithm for classification of cross-country (XC) ski-skating gears (G) using Smartphone accelerometer data. Eleven XC skiers (seven men, four women) with regional-to-international levels of performance carried out roller skiing trials on a treadmill using fixed gears (G2left, G2right, G3, G4left, G4right) and a 950-m trial using different speeds and inclines, applying gears and sides as they normally would. Gear classification by the Smartphone (on the chest) and based on video recordings were compared. Formachine-learning, a collective database was compared to individual data. The Smartphone application identified the trials with fixed gears correctly in all cases. In the 950-m trial, participants executed 140 ± 22 cycles as assessed by video analysis, with the automatic Smartphone application giving a similar value. Based on collective data, gears were identified correctly 86.0% ± 8.9% of the time, a value that rose to 90.3% ± 4.1% (P < 0.01) with machine learning from individual data. Classification was most often incorrect during transition between gears, especially to or from G3. Identification was most often correct for skiers who made relatively few transitions between gears. The accuracy of the automatic procedure for identifying G2left, G2right, G3, G4left and G4right was 96%, 90%, 81%, 88% and 94%, respectively. The algorithm identified gears correctly 100% of the time when a single gear was used and 90% of the time when different gears were employed during a variable protocol. This algorithm could be improved with respect to identification of transitions between gears or the side employed within a given gear.

Place, publisher, year, edition, pages
MDPI AG , 2014. Vol. 14, no 11, p. 20589-20601
Keywords [en]
Algorithm, Collective classification, Gaussian filter, Individual classification, Machine learning, Markov chain, Smartphone, Accelerometers, Algorithms, Artificial intelligence, Learning systems, Markov processes, Signal encoding, Smartphones, Video recording, Accelerometer data, Automatic algorithms, Automatic classification, Automatic procedures, Collective classifications, Gaussian filters, Smart-phone applications, Variable protocols, Gears
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Engineering and Technology
Identifiers
URN: urn:nbn:se:ri:diva-45484DOI: 10.3390/s141120589Scopus ID: 2-s2.0-84908530187OAI: oai:DiVA.org:ri-45484DiVA, id: diva2:1457401
Available from: 2020-08-11 Created: 2020-08-11 Last updated: 2023-05-09Bibliographically approved

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Holst, AndersJonasson, Arndt

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