Simulated cycling data set and musculoskeletal models

This study used musculoskeletal modelling to explore the relationship between cycling conditions (power output and cadence) and muscle activation and metabolic power. We hypothesized that the cadence that minimized the simulated average active muscle volume would be higher than that which minimized the simulated metabolic power. We validated the simulation by comparing predicted muscle activation and fascicle velocities with experimental electromyography and ultrasound images. We found strong correlations for averaged muscle activations and moderate to good correlations for fascicle dynamics. These correlations tended to weaken when analyzed at the individual participant level. Our study revealed a curvilinear relationship between average active muscle volume and cadence, with the minimum active volume being aligned to the self-selected cadence. The simulated metabolic power was consistent with previous results and was minimized at lower cadences than that which minimized active muscle volume across power outputs. Whilst there are some limitations to the musculoskeletal modelling approach, the findings suggest that minimizing active muscle volume may be a more important factor than minimizing metabolic power for self-selected cycling cadence preferences. Further research is warranted to explore the potential of an active muscle volume-based objective function for control schemes across a wider range of cycling conditions.

Methods
The data set was collected through an experimental and simulated paradigm.

The experimental data were extracted from Riveros-Matthey et al. (Riveros-Matthey et al. 2023). The study considered twelve level 3–4 cyclists who rode 20-sec cycling bouts in an ergometer under 10%, 30% and 50% of the Pmax at 60,70, 80, 90, 100, 110, 120 and the SSC.  Kinematic and kinetic data were measured using 3D motion capture and force-instrumented pedals. The vastus lateralis (VL) shortening velocities were measured using two ultrasound transducers, held in series via a 3D printed frame. EMG signals were recorded wirelessly from gluteus maximus (GMAX), vastus lateralis (VL), rectus femoris (RF), biceps femoris (BF) and semitendinosus (ST), tibialis anterior (TA), soleus (SOL) and gastrocnemius medialis (GM). 

During the simulated paradigm, a right lower extremity model was driven by 40 Hill-type muscle-tendon unit actuators (Millard et al., 2013), including muscular energetic probes (Umberger, 2010). After an inverse dynamic approach, a prescribed simulation (OpenSim Moco) was implemented, aiming to solve the muscular redundancy problem.

Data processing
1. To validate the model, a comparison between experimental data (EMG activations and the VL fascicle shortening velocity per cycle) and those predicted (n=6) were considered using R2 and linear mixed models.

2. Active muscle volume and metabolic expenditure were processed through the simulation outputs and Muscle Analysis tool (OpenSim).