Stochastic modelling of muscle control during shoulder abduction

Abstract

The intrinsic variability of the shoulder joint motion is a critical factor in the characterisation of the shoulder joint. However, traditional computational approaches struggle to account for it. On the other hand, the stochastic approach allows to identify a set of plausible solutions. In this study, the Myobolica toolbox, which yielded promising results in the lower limb, was employed to simulate a shoulder abduction, with twofold aims: to assess its generalisability to other joints, and to evaluate an electromyography (EMG)-informed version of Myobolica.

Publicly available kinematics, EMG, and glenohumeral (GH) joint force data measured by an instrumented implant on a 64-year-old man executing three weighted shoulder abductions were used. A previously developed shoulder musculoskeletal model was employed to compute stochastic simulations informed and not with EMG data, sampling 1 × 10⁵ solutions every 30 timeframes. The predicted GH joint force were compared to the experimental data, and the variance in the solutions across simulations was computed.

Overall, the correlation between the GH joint force predicted by Myobolica and the experimental values increased when the EMG-based constraint was applied (from approximately R² = 0.06, RMSE = 1.82 BW to R² = 0.6, RMSE = 0.73 BW when all available EMG data were employed). Using EMG led to a reduction (from 2.3 to 0.65 BW) in the solution bandwidths.

Providing EMG data to inform the simulations helped improve their accuracy. However, the results obtained otherwise remain promising. Additional work is required to minimize the computational cost of the Myobolica approach. A consistency gap between experimental data and the model is reported.