Quantifying workload using nonlinear dynamical measures of biomechanical parameters during cycling on a roller...

Area of Science:

• Biomechanics
• Nonlinear dynamics
• Sports science

Background:

• Distinguishing individual workload in cycling is crucial for performance optimization and training.
• Nonlinear dynamics offer novel approaches to analyze complex physiological systems like human locomotion.
• Existing methods may not fully capture the intricate variations in cycling biomechanics under different loads.

Purpose of the Study:

• To evaluate the effectiveness of nonlinear parameters, specifically ML1 and maximum Lyapunov exponents, in differentiating individual workload levels during cycling.
• To test if ML1 derived from kinematic data (ML1α) is comparable to that from force data (ML1F).
• To determine if increasing cycling load decreases local system stability, as indicated by Lyapunov exponents.

Main Methods:

• A maximal incremental cycling step test was performed on 10 participants using an ergometer.
• Pedaling torque and kinematic crank data were collected.
• Nonlinear parameters ML1F, ML1α, and Lyapunov exponents (λst, λlt, ιst, ιlt) were calculated at comparable load levels.

Main Results:

• ML1α showed a significant linear increase across three load levels, comparable to ML1F.
• A linearly increasing trend was observed for the short-term Lyapunov exponent (λst) across load levels.
• The intercepts (ιst, ιlt) for short- and long-term divergence demonstrated a statistically significant linear increase with higher loads.

Conclusions:

• Nonlinear parameters, including ML1α and Lyapunov exponents, are suitable for distinguishing individual workload levels in cycling.
• Increased cycling load is associated with decreased local system stability.
• These findings could inform the development of advanced e-bike propulsion systems.