INTRODUCTION: Alpine skiing is a popular sport at both amateur and competitive levels, characterized by complex biomechanics. However, research on skiing biomechanics is limited due to the challenges of replicating real-world conditions in controlled environments and conducting on-field studies where conditions are variable. Technological advancements have made it easier to use equipment even in difficult environments. In movement analysis, motion capture (MoCap) systems provide valuable data for training and injury prevention, with passive marker-based systems being the gold standard in laboratories. However, these systems are less effective outdoors due to sunlight and snow reflections1, while inertial MoCap systems are more practical for outdoor conditions2. This study compares various measurement systems for assessing skier biomechanics, focusing on their effectiveness in capturing key movement parameters and forces in both simulated and real on-slope conditions. METHODS: Six subjects performed nine trials, each lasting 20 seconds, during which they executed flexion-extension movements of the lower limbs to simulate the technical gesture involved in skiing. The trials were conducted on an inertial system (YoYo, Hance, Sweden) equipped with ski bindings, and the subjects were fitted with an X-sens system (Movella) to derive the ankle flexion angle. Additionally, markers were placed on the ski boots and the tibial tuberosity of the subjects, which were recorded by six cameras of the MoCap system (Qualisys) to obtain a reference angular value. The forces exerted by the subjects were measured using two methods: four load cells (Fyearfly 200kg) mounted between the ski bindings and the inertial system, two for each side, one in the front and the other in the rear of the binding, and sensorized insoles (Pedar Loadsol-mlp) placed inside the ski boots. RESULTS/DISCUSSION: The comparison between the two motion capture systems showed excellent results in detecting ankle angle variation. The values provided by both systems, a significant correlation (p < .001) and a strong linear relationship (R² = 0.91). The analysis of the force data revealed that the load cells measured higher force values than the insoles (mean value 709.5 ± 239.8 N vs. 401.6 ± 135.8 N, respectively). This difference may be due to the placement of the load cells under the bindings, while the insoles were located inside the ski boots. We can hypothesize that the force detected by the load cells is the sum of what is detected by the insoles plus the load transmitted from the leg through the ski boot. However, the correlation between the two data sets was statistically significant (p < .001). Notably, a moderately strong quadratic fit (R² = 0.67) implies that there is a non-linear relationship that captures the variance in the data, further supporting the relationship among the observed data. CONCLUSION: The study demonstrates that both motion capture systems effectively detect ankle angle variations with a strong correlation. While the load cells measure higher forces, the significant correlation with insole data suggests that insoles reproduce approximately 70% of the load cell measurements. Therefore, they can be used for measurements during on-slope skiing, but the results should be analyzed with caution, as they do not precisely reflect the forces exerted on the skis.
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