Abstract
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.
Highlights
New technologies, such as mobile Internet and Smartphones, with sensors, social media and networking, offer novel opportunities for developing intelligent tools for use in sports science and training
Two approaches to gear classification were utilized: (1) individual classification of gears on the basis of the data from the first trial and (2) classification based on collective data obtained in advance during various training sessions on snow and roller skis by different skiers
The input data appear to contain the information required for a machine-learning algorithm to solve the task
Summary
New technologies, such as mobile Internet and Smartphones, with sensors, social media and networking, offer novel opportunities for developing intelligent tools for use in sports science and training. The introduction of the iPhone in 2007 opened a new era of user-IT interface, including thousands of mobile phone applications (apps), that has significantly altered the relationship of ordinary people to advanced technology, making it an integrated part of their daily lives These advances led to the first generation of apps designed for sports and fitness, including apps that monitor routes, distances and styles of running, technique in various sporting activities, and fitness or training records. Both easy to use and readily accessible, these apps utilize the internal sensors and actuators in Smartphones, including GPS, accelerometers, cameras and sensors of sound and vibration, providing immediate and high-impact display of the results. The data collected can be transmitted in real-time to cloud-based services for discerning patterns, providing immediate rather than the delayed feedback associated with laboratory monitoring
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