Validation of a Sensor-Based Dynamic Ski Deflection Measurement in the Lab and Proof-of-Concept Field Investigation

Abstract

Introduction: Ski deflection is a performance-relevant factor in alpine skiing and the segmental and temporal curvature characteristics (m−1) along the ski have lately received particular attention. Recently, we introduced a PyzoFlex® ski deflection measurement prototype that demonstrated high reliability and validity in a quasi-static setting. The aim of the present work is to test the performance of an enhanced version of the prototype in a dynamic setting both in a skiing-like bending simulation as well as in a field proof-of-concept measurement. Material and methods: A total of twelve sensor foils were implemented on the upper surface of the ski. The ski sensors were calibrated with an empirical curvature model and then deformed on a programmable bending robot with the following program: 20 times at three different deformation velocities (vslow, vmedium, vfast) with (1) central bending, (2) front bending, (3) back bending, (4) edging left, and (5) edging right. For reliability assessment, pairs of bending cycles (cycle 1 vs. cycle 10 and cycle 10 vs. cycle 20) at vslow, vmedium, and vfast and between pairs of velocity (vslow vs. vmedium and vslow vs. vfast) were evaluated by calculating the change in the mean (CIM), coefficient of variation (CV) and intraclass correlation coefficient (ICC 3.1) with a 95% confidence interval. For validity assessment, the calculated segment-wise mean signals were compared with the values that were determined by 36 infrared markers that were attached to the ski using an optoelectrical measuring system (Qualisys). Results: High reliability was found for pairs of bending cycles (CIM −0.69–0.24%, max CV 0.28%, ICC 3.1 > 0.999) and pairs of velocities (max CIM = 3.03%, max CV = 3.05%, ICC 3.1 = 0.997). The criterion validity based on the Pearson correlation coefficient was r = 0.98. The accuracy (systematic bias) and precision (standard deviation), were −0.003 m−1 and 0.047 m−1, respectively. Conclusions: The proof-of-concept field measurement has shown that the prototype is stable, robust, and waterproof and provides characteristic curvature progressions with plausible values. Combined with the high laboratory-based reliability and validity of the PyzoFlex® prototype, this is a potential candidate for smart ski equipment.

Technique-Dependent Relationship between Local Ski Bending Curvature, Roll Angle and Radial Force in Alpine Skiing

“…In contrast, during a parallel ski steering turn, a point along the edge does not follow the path of the proceeding ski but slides laterally across the slope [4,[7][8][9]. For practical reasons, the majority of the literature refers to parallel ski steering or carving, but these processes are not dichotomous; rather, both can occur simultaneously in different segments along the ski, depending on the so-called angle of attack [4,10]. To ensure that the angle of attack is as small as possible, the vector of the skier's translational velocity must always point in the direction of the orientation of the ski [4,7].…”

“…Equation ( 1) presents a generalized representation of the ski's deformation, as the ski's bending characteristics are not homogeneous but rather exhibit variations in segmental curvature along its length [10,13]. Thus, to represent the inhomogeneous deflection pattern, it would be an oversimplification to fit a circle with a constant radius to the ski's bending line.…”

Study of Pressure Distribution in Floor Tiles with Printed P(VDF:TrFE) Sensors for Smart Surface Applications

“…Current technology trends require a higher presence of pressure sensors integrated into their surroundings with the aim to increase the degree of automation or to enable condition monitoring [ 1 , 2 , 3 ]. Be it for medical applications [ 4 ], industrial environments [ 1 ], positional tracking [ 2 , 5 , 6 , 7 ], or other fields [ 8 , 9 ], pressure sensors have become ubiquitous. In smart building/home environments, pressure sensitive floor tiles open new possibilities of movement, occupancy, and event detection without the need of privacy-violating video surveillance, which is especially relevant in non-public areas, where video surveillance is forbidden.…”

Sensor Technology for Sports Monitoring

“…It consists of ten papers related to all kinds of sensors that are currently being used for monitoring different sports. The topics vary from dancing to winter sports such as figure skating, alpine and cross-country skiing with para-athletic and able-bodied skiers, to summer sports such as football and kayaking.The aim of the first article [1] in this Special Issue was to test the performance of an enhanced version of a prototype in a dynamic skiing-like bending simulation as well as in a proof-of-concept field measurement. It was concluded that combined with the high laboratory-based reliability and validity of the PyzoFlex® prototype, it can be a potential candidate for smart ski equipment.In [2], a system for detecting and visualizing the very important dance motions known as stops was introduced.…”

“…The aim of the first article [ 1 ] in this Special Issue was to test the performance of an enhanced version of a prototype in a dynamic skiing-like bending simulation as well as in a proof-of-concept field measurement. It was concluded that combined with the high laboratory-based reliability and validity of the PyzoFlex ® prototype, it can be a potential candidate for smart ski equipment.…”