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Automatic classification of the Sub-Techniques (Gears) used in cross-country ski skating employing a mobile phone

Stöggl, Thomas (författare)
Mittuniversitetet,Avdelningen för hälsovetenskap,Department of Sport Science and Kinesiology, University of SalzburgHallein/Rif, Austria,Swedish Winter Sports Research Centre,Mittuniversitetet, Avdelningen för hälsovetenskap
Holst, Anders (författare)
KTH,RISE,SICS,KTH Royal Institute of Technology, Sweden,Beräkningsbiologi, CB,Swedish Institute of Computer Science, Sweden,School of Computer Science and Communication, Royal Institute of Technology, Stockholm, Sweden
Jonasson, Arndt (författare)
Karolinska Institutet,RISE,SICS,Swedish Institute of Computer Science, Kista, Sweden
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Andersson, Erik (författare)
Mittuniversitetet,Avdelningen för hälsovetenskap,Swedish Winter Sports Research Centre,Mittuniversitetet, Avdelningen för hälsovetenskap
Wunsch, Tobias (författare)
University of Salzburg, Austria
Norström, Christer (författare)
RISE,SICS,Swedish Institute of Computer Science, Kista, Sweden
Holmberg, Hans-Christer, 1958- (författare)
Mittuniversitetet,Avdelningen för hälsovetenskap,Swedish Olympic Committee, Stockholm, Sweden,Swedish Winter Sports Research Centre,Mittuniversitetet, Avdelningen för hälsovetenskap
Wunsch, Thomas (författare)
Department of Sport Science and Kinesiology, University of SalzburgHallein/Rif, Austria
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 (creator_code:org_t)
2014-10-31
2014
Engelska.
Ingår i: Sensors. - : MDPI AG. - 1424-8220. ; 14:11, s. 20589-20601
  • Tidskriftsartikel (refereegranskat)
Abstract Ämnesord
Stäng  
  • 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.

Ämnesord

TEKNIK OCH TEKNOLOGIER  -- Annan teknik (hsv//swe)
ENGINEERING AND TECHNOLOGY  -- Other Engineering and Technologies (hsv//eng)
MEDICIN OCH HÄLSOVETENSKAP  -- Hälsovetenskap -- Idrottsvetenskap (hsv//swe)
MEDICAL AND HEALTH SCIENCES  -- Health Sciences -- Sport and Fitness Sciences (hsv//eng)
TEKNIK OCH TEKNOLOGIER  -- Elektroteknik och elektronik -- Signalbehandling (hsv//swe)
ENGINEERING AND TECHNOLOGY  -- Electrical Engineering, Electronic Engineering, Information Engineering -- Signal Processing (hsv//eng)
TEKNIK OCH TEKNOLOGIER  -- Elektroteknik och elektronik -- Telekommunikation (hsv//swe)
ENGINEERING AND TECHNOLOGY  -- Electrical Engineering, Electronic Engineering, Information Engineering -- Telecommunications (hsv//eng)

Nyckelord

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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ref (ämneskategori)
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