DOI: 10.5061/dryad.p8cz8w9wx
title: Simulated cycling data set and musculoskeletal models

description: 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.
description: 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).
description: # Simulated-Cycling-Data
 [https://doi.org/10.5061/dryad.p8cz8w9wx](https://doi.org/10.5061/dryad.p8cz8w9wx) To study cycling muscle dynamics, we employed the OpenSim direct collocation method. This dataset validates the approach, offering data on metabolic power, VL muscle fascicle lengths, excitations, activations, and muscle forces for 40 actuators across 6 participants. It also includes scaled musculoskeletal models. This is shared using GitFront. The data was stored as .sto files. Each model was stored as .osim files