Abstract
Position–time tracking of athletes during a race can provide useful information about tactics and performance. However, carrier-phase differential global navigation satellite system (dGNSS)-based tracking, which is accurate to about 5 cm, might also allow for the extraction of variables reflecting an athlete’s technique. Such variables include cycle length, cycle frequency, and choice of sub-technique. The aim of this study was to develop a dGNSS-based method for automated determination of sub-technique and cycle characteristics in cross-country ski skating. Sub-technique classification was achieved using a combination of hard decision rules and a neural network classifier (NNC) on position measurements from a head-mounted dGNSS antenna. The NNC was trained to classify the three main sub-techniques (G2–G4) using optical marker motion data of the head trajectory of six subjects during treadmill skiing. Hard decision rules, based on the head’s sideways and vertical movement, were used to identify phases of turning, tucked position and G5 (skating without poles). Cycle length and duration were derived from the components of the head velocity vector. The classifier’s performance was evaluated on two subjects during an in-field roller skiing test race by comparison with manual classification from video recordings. Classification accuracy was 92–97% for G2–G4, 32% for G5, 75% for turning, and 88% for tucked position. Cycle duration and cycle length had a root mean square (RMS) deviation of 2–3%, which was reduced to <1% when cycle duration and length were averaged over five cycles. In conclusion, accurate dGNSS measurements of the head’s trajectory during cross-country skiing contain sufficient information to classify the three main skating sub-techniques and characterize cycle length and duration.
Highlights
Global Navigation Satellite Systems (GNSS) are frequently used to study performance in racing and endurance sports
The results were similar for cycle length (1.9 ± 0.3%, 2.2 ± 0.7% and 3.1 ± 1.0% for G2, G3 and G4, respectively), indicating that the discrepancies were mostly due to the estimates of cycle duration and not due to differences in the displacement of the head and center of mass (CoM) (Figure 6, bottom row)
Even if the neural network classifier (NNC) would have a higher sensitivity for classifying G5 compared to the simple threshold method used in the current study, overall classification performance might deteriorate because the more frequently used G2–G4 can be misclassified as G5
Summary
Global Navigation Satellite Systems (GNSS) are frequently used to study performance in racing and endurance sports. The results can be presented live to spectators or used post session in performance or tactical analyses for practitioners and scientists. A performance analysis typically entails a continuous comparison of time gained or lost between competitors throughout the race [1]. Knowledge about when and where athletes gain or lose time compared to their opponents has limited value unless the reasons for the time loss or gain can be elucidated. In cross-country ski racing, factors explaining differences in performance include physiological, tactical (i.e., pacing [2]) and technical aspects. Recent studies have shown that estimating metabolic energy expenditure [3,4] and conducting performance analyses [1] can be achieved by using GNSS measurements
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