ECSS Paris 2023: OP-BM16
INTRODUCTION: Recent work introduced the concept of a "smart luge", demonstrating that steering actions can be captured using force sensors placed at key interaction points between athlete and sled [1]. While these studies focused on feasibility, quantitative models linking athlete input to runner contact behavior are still lacking in the literature. Such models are relevant, as runner deformation and contact conditions directly influence ice friction and sled dynamics. The aim of this pilot study was to develop and apply a fully instrumented luge to capture athlete force input, runner deformation, inter-runner spacing, and runner-ground contact mechanics, and to establish a quantitative input-output model of sled steering. We hypothesized that shoulder and bow forces would systematically predict runner contact center position and contact length. METHODS: A luge was instrumented with force sensors at the shoulders and bows, strain gauges on both runners (front section) to capture runner deformation, and linear potentiometers to quantify the relative distance between the left and right runners. Runner-ground contact mechanics were derived from stabilized video recordings based on the absence of transmitted light underneath the runner, yielding contact length and contact center position for each runner. In a controlled laboratory setup, an experienced former Olympic-level athlete performed 20 quasi-static steering cycles at three steering intensities in simulated left and right turns. Signals were segmented into steering cycles and time-normalized. Standardized multiple regression analyses using athlete force inputs were applied to quantify input-output relationships between athlete forces and mechanical contact measures. RESULTS: Contact center position was explained almost completely by athlete force inputs alone, with adjusted R2 values ranging from 0.93 to 0.98. Standardized regression coefficients showed direction-dependent dominance, with bow forces prevailing in left turns and shoulder forces in right turns (absolute beta = 0.80-0.94, all p < 0.001). In contrast, contact length showed lower and more variable explainability and did not exhibit systematic modulation across steering conditions. CONCLUSION: This study demonstrates the feasibility of a fully instrumented luge for quantitative input-output modeling of sled steering. Steering-related contact mechanics, particularly contact-center modulation, can be described by a compact model driven primarily by athlete force application. The observation that direction-dependent force dominance occurred even in an experienced former Olympic-level athlete suggests that distinct steering strategies may be applied implicitly rather than consciously and could be addressed in simulator-based and dry-land training using objective force-based feedback. References 1. Staudinger R, Kremser W, Thorwartl C, Schwameder H. Towards a smart luge that measures steering input of the rider. Proc 12th Int Conf Sport Science Research and Technology Support. 2024.
Read CV Severin BernhartECSS Paris 2023: OP-BM16
INTRODUCTION: Peak instantaneous power during cycling occurs progressively later in the crank cycle as cadence increases. This cadence-dependent shift is commonly attributed to neuromuscular timing constraints related to force-development dynamics and electromechanical delay. Upper-limb forces play an important stabilizing role during high-intensity cycling and facilitate the attainment of maximal power output, yet their influence on the timing of peak power within the crank cycle remains unclear. The present study aimed to quantify the relationship between cadence and crank angle at peak power and to determine whether upper-body involvement modulates this relationship. METHODS: Forty cyclists spanning a wide performance range performed paired maximal seated efforts on an isokinetic ergometer under two conditions: intentional handlebar pulling and instructed avoidance of pulling. In the non-pulling condition, pulling was additionally constrained by an open-palm hand posture to minimize force application at the handlebar. Cadence was systematically varied from 60 to 120 rpm in 15-rpm increments. Peak crank angle was defined as the angle of maximal instantaneous power within each participant’s highest-power revolution. Linear mixed-effects models with random intercepts and slopes were used to characterize cadence-dependent timing and between-condition differences. RESULTS: Peak-power crank angle increased linearly with cadence in both conditions, with a 42 percent greater slope when pulling was allowed (0.356 vs 0.251 deg per rpm; difference = 0.105 deg per rpm, 95 percent CI 0.038 to 0.174, p = 0.002, Cohen's d = 0.85). Between-participant intercept variability was reduced by 58 percent when pulling was prevented (SD 4.5 vs 10.6 deg, p < 0.001), indicating that upper-body involvement accounts for a large proportion of inter-individual differences in peak-power timing. Within-participant residual variability increased by 34 percent without pulling (SD 8.1 vs 6.1 deg, p < 0.001), reflecting reduced timing consistency. The observed slopes, when expressed in temporal units by converting angular shift per rpm into milliseconds, correspond to 42 and 59 ms respectively, values that are physiologically plausible and that situate the mechanically derived cadence-timing relationship within neuromuscular time scales. CONCLUSION: Peak-power crank angle follows a robust linear cadence-dependent timing law during maximal seated cycling. Upper-body involvement significantly amplifies cadence sensitivity while reducing within-individual variability, suggesting a stabilizing role in timing control. These findings indicate that upper-body technique influences not only maximal power capacity but also the precision of power-phase timing during high-intensity cycling.
Read CV Michael BaharECSS Paris 2023: OP-BM16