Connected skiing: Validation of edge angle and radial force estimation as motion quality parameters during alpine skiing

Authors:
Open Access
Publisher website
Google Scholar
Abstract
Recent studies have developed wearable sensor systems to detect, classify and evaluate performance during alpine skiing. In order to enrich skiing data to provide motion quality feedback, edge angle (EA) and radial force (Fr) are parameters of interest. However, the estimation of these parameters via calibration‐free wearable technologies has not been validated. The purpose of this study was to develop and validate a wearable method to estimate EA and Fr. Participants completed simulated skiing trials on an indoor skiing carpet. Two IMU's mounted to the ski boots estimated EA and Fr and compared to reference values measured with a 3D motion capture system. The performance of the wearable system was quantified by accuracy and precision. The overall accuracy and precision of the wearable system was 97.6 ± 12.4% and 15.5 ± 17.6% for EA, and 105.5 ± 5.7% and 29.8 ± 10.0%, respectively for Fr. The developed wearable system was accurate for the estimation of EA and Fr, but was highly variable with low precision for both metrics. Further research is needed to improve the precision of field‐relevant skiing metrics during in‐field studies using simple measurement setups that can easily be implemented by recreational and expert skiers alike.Highlights IMU's mounted on the boots are sufficient tools for accurate estimation of edge angle and radial force during both long and short style turns on a skiing simulator. As the estimation of edge angle and radial force are dependent on other estimated parameters (i.e. turn switch), the precision of these metrics is relatively low. The results of the current study apply only to simulated alpine skiing on a treadmill, and further work is required to prove the accuracy and precision of this system on snow.
Funding Information
  • Austrian Ministry for Transport, Innovation and Technology
  • Österreichische Forschungsförderungsgesellschaft

This publication has 34 references indexed in Scilit: