It is recommended that users first become especially familiar with this toolbox before using syner-gp.ĭetails of approximating muscle synergy functions using syner-gp is specified by users according to a project.Ī project specifies all aspects related to a given syner-gp session including data import, data pre-processing, synergy model structure (gp model and input muscle structure), validation, and evaluation. The syner-gp toolbox essentially organizes data in accordance with the notion of a synergy function so that a Gaussian process regression model can be developed for function approximation.Īll aspects of GPR model training is performed using the GPML toolbox. McGinnis, "A Gaussian Process Model of Muscle Synergy Functions for Estimating Unmeasured Muscle Excitations Using a Measured Subset," in IEEE Transactions on Neural Systems and Rehabilitation Engineering, doi: 10.1109/TNSRE.2020.3028052. For theoretical development and validation see, R. )ĭescription: syner-gp streamlines the development of muscle synergy function models which describe the relationship between excitations of a subset of 'input' muscles and an output muscle.
Rasmussen and Hannes Nickisch, "Gaussian Processes for Machine Learning (GPML) Toolbox," Journal of Machine Learning Research, vol. Requirements: MATLAB R2019 or later, MATLAB Signal Processing Toolbox, GPML Toolbox (Carl E. Author: Reed Gurchiek, use of this toolbox please cite: R.
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