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In-Field Validation of an Inertial Sensor-Based System for Movement Analysis and Classification in Ski Mountaineering.

  • Gellaerts J UFR Sciences & Montagne (SceM), Département STAPS, Université Savoie Mont-Blanc, Campus Scientifique Technolac, 73376 Le Bourget du Lac, France. jules.gellaerts@gaitup.com.
  • Bogdanov E Gait Up SA, EPFL Innovation Park, CH-1015 Lausanne, Switzerland. evgeny.bogdanov@crealogix.com.
  • Dadashi F Gait Up SA, EPFL Innovation Park, CH-1015 Lausanne, Switzerland. farzin.dadashi@gaitup.com.
  • Mariani B Gait Up SA, EPFL Innovation Park, CH-1015 Lausanne, Switzerland. benoit.mariani@gaitup.com.
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  • 2018-03-17
Published in:
  • Sensors (Basel, Switzerland). - 2018
Ski mountaineering
inertial sensors
movement analysis
real time measurement
Algorithms
Biomechanical Phenomena
Humans
Mountaineering
Movement
Skiing
English Ski Mountaineering (SkiMo) is a fast growing sport requiring both endurance and technical skills. It involves different types of locomotion with and without the skis. The aim of this study is to develop and validate in the snowfield a novel inertial-based system for analysing cycle parameters and classifying movement in SkiMo in real-time. The study was divided into two parts, one focused on real-time parameters estimation (cadence, distance from strides, stride duration, stride length, number of strides, slope gradient, and power) and, second, on transition detection (kickturns, skin on, skin off, ski on and off backpack) in order to classify between the different types of locomotion. Experimental protocol involved 16 experienced subjects who performed different SkiMo trials with their own equipment instrumented with a ski-mounted inertial sensor. The results obtained by the algorithm showed precise results with a relative error near 5% on all parameters. The developed system can, therefore, be used by skiers to obtain quantitative training data analysis and real-time feedback in the field. Nevertheless, a deeper validation of this algorithm might be necessary in order to confirm the accuracy on a wider population of subjects with various skill levels.
Language
  • English
Open access status
gold
Identifiers
Persistent URL
https://folia.unifr.ch/global/documents/105686
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