FingClr

Machine learning ( RNN (Recurrent Neural Network) and SVM (Support Vector Machine) ) recognition/classification of 7 hand gestures using 6 channels of BioRadio 150 & BioCapture.

Note: This is for educational purposes, as part of the coursework for the Biorobotics & Cybernetics Course at RIT.

Detailed Report on Implementation

• FingClr Report
• FingClr Presentation

Quick Start

• Download the repo
• Open MatLab and change path to directory of repo
• Open DEMO.m and edit DataDir and ValidationDataDir to match the locations of the data on your computer
• Run Options
  • UseValidationData : Set to 1 to train and test on the validation dataset. Set to 0 to use the collected EMG data.
  • NeedDataForPlots : Set to 1 if training the SVM. Optionally set to 1 for training the RNN, if you want to view plots of the data.
  • DemoCNN : Set to 1 to train a new RNN. Models with testing accuracies greater than 90% are automatically saved to the Models folder.
  • DemoSVM : Set to 1 to train a new SVM. Models with testing accuracies greater than 90% are automatically saved to the Models folder.
  • RunCount : How many times the model(s) should be trained before the overall confusion matrices are generated.
  • TestRatio : What ratio of the data to reserve for testing.
  • ShowModelDemo : Set to 1 to load an SVM trained model / RNN trained model. You will need to modify the paths in the DEMO.m file.
  • BuildModels : Set to 1 to build new models. Set to 0 to skip building of new models.