Introduction From the perspective of dynamic systems theory, stability and variability of biological signals are both understood as a functional adaptation to variable environmental conditions. In the present study, we examined whether this theoretical perspective is applicable to the pedalling movement in cycling. Non-linear measures were applied to analyse pedalling forces with varying levels of subjective load. Materials and methods Ten subjects completed a 13-sector virtual terrain profile of 15 km total length on a roller trainer with varying degrees of virtual terrain inclination (resistance). The test was repeated two times with different instructions on how to alter the bikes gearing. During the experiment, pedalling force and heart rate were measured. Force-time curves were sequenced into single cycles, linearly interpolated in the time domain, and z-score normalised. The established time series was transferred into a two-dimensional phase space with limit cycle properties given the applied 25% phase shift. Different representations of the phase space attractor were calculated within each sector and used as non-linear measures assessing pedalling forces. Results and discussion A contrast analysis showed that changes in pedalling load were strongly associated to changes in non-linear phase space attractor variables. For the subjects investigated in this study, this association was stronger than that between heart rate and resistance level. The results indicate systematic changes of the pedalling movement as an adaptive response to an externally determined increase in workload. Future research may utilise the findings from this study to investigate possible relationships between subjective measures of exhaustion, comfort, and discomfort with biomechanic characteristics of the pedalling movement and to evaluate connections with dynamic stability measures.
The study of biomechanical and physiological variables allows human movement scientists to investigate the characteristics of movement patterns in cyclists. While straightforward and well-investigated, linear measures may not adequately capture the underlying complexity of movements during cycling. Therefore, a non-linear phase-space measure (ML1) was applied to forces and heart rates of nine cyclists in a field test. The test was repeated two times with different instructions on how to alter the bike’s gearing while cycling a 12.5 km long level track. Pedaling forces and heart rates were measured. Future aim of the underlying study is the investigation of innovative control algorithms for e-bike propulsions. Force-time curves were sequenced into single cycles, linearly interpolated in the time domain, and z-score normalized. ML1 was calculated in a flowing window algorithm of 50 cycles. With fixed gearing, a contrast analysis showed that changes in terrain inclination were strongly associated with changes in the non-linear measure ML1. The calculation of Spearman’s cross correlation showed high coefficients of correlation between ML1 and delayed heart rate. The results indicate systematic changes of the pedaling movement as an adaptive response to changes in terrain inclination. Future research may utilize the findings from this study to investigate possible relationships between subjective measures of exhaustion, comfort, and discomfort with biomechanical characteristics of the pedaling movement.
The aim of the present study was to determine the effectiveness of nonlinear parameters in distinguishing individual workload in cycling by using bike-integrated sensor data. The investigation focused on two nonlinear parameters: The ML1, which analyzes the geometric median in phase space, and the maximum Lyapunov exponent as nonlinear measure of local system stability. We investigated two hypothesis: 1. ML1α, derived from kinematic crank data, is as good as ML1F, derived from force crank data, at distinguishing between individual load levels. 2. Increasing load during cycling leads to decreasing local system stability evidenced by linearly increasing maximal Lyapunov exponents generated from kinematic data. A maximal incremental cycling step test was conducted on an ergometer, generating complete datasets from 10 participants in a laboratory setting. Pedaling torque and kinematic data of the crank were recorded. ML1F, ML1α, and Lyapunov parameters (λst, λlt, ιst, ιlt) were calculated for each participant at comparable load levels. The results showed a significant linear increase in ML1α across three individual load levels, with a lower but still large effect compared to ML1F. The contrast analysis also confirmed a linearly increasing trend for λst across three load levels, but this was not confirmed for λlt. However, the intercepts ιst and ιlt of the short- and longterm divergence showed a statistically significant linear increase across the load levels. In summary, nonlinear parameters seem fundamentally suitable to distinguish individual load levels in cycling. It is concluded that higher load during cycling is associated with decreasing local system stability. These findings may aid in developing improved e-bike propulsion algorithms. Further research is needed to determine the impact of factors occurring in field application.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.