Estimation of Alpine Skier Posture Using Machine Learning Techniques

Abstract

High precision Global Navigation Satellite System (GNSS) measurements are becoming more and more popular in alpine skiing due to the relatively undemanding setup and excellent performance. However, GNSS provides only single-point measurements that are defined with the antenna placed typically behind the skier's neck. A key issue is how to estimate other more relevant parameters of the skier's body, like the center of mass (COM) and ski trajectories. Previously, these parameters were estimated by modeling the skier's body with an inverted-pendulum model that oversimplified the skier's body. In this study, we propose two machine learning methods that overcome this shortcoming and estimate COM and skis trajectories based on a more faithful approximation of the skier's body with nine degrees-of-freedom. The first method utilizes a well-established approach of artificial neural networks, while the second method is based on a state-of-the-art statistical generalization method. Both methods were evaluated using the reference measurements obtained on a typical giant slalom course and compared with the inverted-pendulum method. Our results outperform the results of commonly used inverted-pendulum methods and demonstrate the applicability of machine learning techniques in biomechanical measurements of alpine skiing.

Tracking Performance in Endurance Racing Sports: Evaluation of the Accuracy Offered by Three Commercial GNSS Receivers Aimed at the Sports Market

“…GNSS solutions aimed at sports with reduced position accuracy requirements (i.e., most wrist-worn receivers or smartphones) apply single frequency analysis to one or two GNSSs in standalone mode ( Terrier et al, 2000 ; Edwards et al, 2002 ; Townshend et al, 2008 ; Jennings et al, 2010a , b ; Wisbey et al, 2010 ; Aughey, 2011 ; Clark et al, 2011 ; Macutkiewicz and Sunderland, 2011 ; Waldron et al, 2011 ; Bolger et al, 2015 ; Sandbakk et al, 2016 ). However, in sports with high demands for position accuracy, geodetic GNSS receivers are used in differential mode using multiple signal frequencies from one or several GNSSs to calculate position, speed, and acceleration ( Larsson and Henriksson-Larsen, 2001 ; Skaloud and Limpach, 2003 ; Wägli, 2009 ; Andersson et al, 2010 ; Supej, 2010 ; Supej and Holmberg, 2011 ; Supej et al, 2012 ; Gilgien et al, 2013 , 2014a , b, 2015a , b ; Bucher Sandbakk et al, 2014 ; Nemec et al, 2014 ; Fasel et al, 2016 ; Kröll et al, 2016 ). Speed can be derived from time differentiation of the position data, or by using the Doppler principle on the GNSS signal ( Zhang et al, 2006 ; Wang and Xu, 2011 ; Boffi et al, 2016 ), acceleration can be derived from position or measured with inertial sensors ( Gilgien et al, 2014b ; Supej and Cuk, 2014 ; Boffi et al, 2016 ).…”

Smart Equipment Design Challenges for Real Time Feedback Support in Sport

“…The important tasks in our research are the selection of appropriate sensors and the assurance of suitable conditions for sensor signal transmission and processing. As it is evident from research papers, IMU sensors are the most often used ones in sports [5][6][7][8][9][10][11][12]. One promising direction of research is the use of smartphones in place of one or several elements of the biofeedback loop.…”

Potential of IMU Sensors in Performance Analysis of Professional Alpine Skiers

“…Although those methodologies have allowed for robust technique analyses, the number of sensor units and total weight of the equipment used appears to be prohibitive for use in daily training environments or during competitions. These issues have been addressed in previous studies, such as [ 20 , 21 ]. Unlike the preceding research, the goal of our study is to identify the best IMU sensor location.…”

“…Nemec et al . [ 21 ] used a machine learning technique to develop reference postures that can be adopted in coaching and training in alpine skiing.…”