6/8/2023 0 Comments Muscle synergy![]() Users can use lib/util/synergp_writeNotes to generate a. These updates are also stored in a cell array field in the session structure called 'notes'. While synergp is running, status updates are constantly printed to the command window. To have access to this, users must set synergyModel.keepTrainingSet = 1 in the specProject_() function (see exampleProject). To use the model later, one also needs the training data. The hyperparameters of model i, for subject s, and muscle m are in session.model(i).subject(s).muscle(m).optimization(end).hyperparameters The main output of the synergp function is a structure (Matlab struct()) called 'session' and users have the option of saving this structure (.mat file) for later analysis (see towards the end of the specProject_exampleProject.m file).Īs described in the specProject_exampleProject.m file, synergy function model characteristics (including GP model details) are specified in the specProject_ function.Īfter synergp has finished, the details of each model trained/tested are in session.model(i).gpModel (i.e. If the GPML toolbox directory ever changes, this file (gpmldir.mat) will need to be deleted. This information is stored in the gpmldir.mat file in lib/util. Users have the option of associating this directory with the GPML toolbox for later use. Upon running synergp the first time, users are first prompted to locate the GPML toolbox (gpml-matlab-v4.2-). The synergp function is the main function used to build synergy function models according to specifications set for any project (set in specProject_()). (5) type 'synergp' in the Command Window and hit 'enter' (return) and follow the indicated prompts (4) make the syner-gp directory the Current Folder in the MATLAB instance ![]() The syner-gp/projects/specProject_exampleProject.m file specifies this example project and users can explore this (verbose) file for understanding how to create new projects to build models. An example dataset for this project is available at. ![]() To best learn how projects are specified and used to control a syner-gp session, users should first explore the exampleProject stored in syner-gp/projects. 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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