Course profile and optimized power distribution for a 75 kg skier.  

Course profile and optimized power distribution for a 75 kg skier.  

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During events involving locomotive exercise, such as cross-country skiing, it is believed that pacing strategies (i.e. power distribution) have a significant impact on performance. Therefore, a program was developed for the numerical simulation and optimization of cross-country ski racing, one that numerically computes the optimal pacing strategy f...

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... Simulations of pacing strategies in endurance sports like cycling, running and cross-coun- try skiing have previously been published by several authors ( Abbiss et al., 2006;Atkinson & Brunskill, 2000;Moxnes, Sandbakk, & Hausken, 2013, 2014Sundström, Carlsson, Ståhl, & Tinnsten, 2012;Sundström, Carlsson, & Tinnsten, 2011;Tucker, Lambert, & Noakes, 2006). Among them, Atkinson and Brunskill (2000) define pacing strategy as the with- in-race distribution of work-rate (power output). ...
... Leino, Spring, and Suominen (1983) reported A′ to be 0.5 m 2 for a semi-squatting skiing position and 0.9 m 2 when skiing upright, whereas Leirdal et al. (2006) estimated A′ = 0.39 m 2 in a semi-squatting posture and A′ = 0.45 m 2 in an upright posture. Carlsson et al. (2011), used the power balance model to simulate XC skiing and further development of the skiing motion equations was completed by Sundström et al. (2011Sundström et al. ( , 2012, when optimising pacing strategies in a synthetic XC skiing race. However, their results were not validated by any experimental studies and it can thus be hypothesised that the propulsive power distribution during actual ski racing differs from the simulated opti- mal strategy. ...
... Still, it is likely that the skiers applied power during e.g., direction changes, but these are not included in the calculations. Over the full race, the average propulsive power in the current study (311, 296, 400 and 386 W) are in line with the results by Sundström et al. (2011Sundström et al. ( , 2012, who reported average propulsive power of 346.8 and 376 W, when simulating the optimal pacing strategy for skiers with body masses of 75 and 82 kg, respectively; this was evaluated for a 1,425 m long XC sprint skiing course. Andersson, Björklund, Holmberg, and Ørtenblad (2017) simulated a 1,300 m long XC sprint race on a treadmill where the mean power output at different stages were between 240 ± 22 W and 445 ± 32 W. ...
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Pacing strategies in cross-country skiing have been investigated in several studies. However, none of the previous studies have been verified by collected skiing data giving the skiing velocities along a measured track. These can be used to calculate the propulsive power output. Collected real-time positioning data from a cross-country sprint skiing race were used to estimate the propulsive power by applying a power balance model. Analyses were made for the time-trial and the final for one female and one male skier. The average propulsive power over the whole race times were 311 and 296 W during the time trial and 400 and 386 W during the final, for the female and male skier, respectively. Compared to the average propulsive power over the whole race, the average active propulsive phases were calculated as 33 and 44% higher in the time trials and 36 and 37% higher in the finals for the female and male, respectively. The current study presents a novel approach to use real-time positioning data to estimate continuous propulsive power during cross-country sprint skiing, enabling in-depth analyses of power output and pacing strategies.
... Here, the average friction coefficients m 1 for HP and LP during the acceleration phase are lower than the m 2 values, employed as a reference. Although these reference values are calculated over a longer distance than proposed by Leino et al. (1983), they are still within the range considered normal for crosscountry skiing, and can thus be regarded as reliable (Moxnes & Hausken, 2009;Saibene et al., 1989;Sundströ m et al., 2011). The m 1 values from the acceleration part of the track fluctuate in a sinusoidal shape for all four trials, Figure 4, which can be Evaluating glide performance in skis 95 considered as an effect of the numerical treatment, but, it can also be hypothesized that the sinusoidal fluctuation can be caused by a 'rolling' position from heel to toe of the test-skier because this change in position and balance could alter velocity and/or friction. ...
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