The Gesture Recognition Toolkit (GRT) is a cross-platform, open-source, C++ machine learning library designed for real-time gesture recognition.

This is the official git repository for the GRT, it is also mirrored on googlecode: https://code.google.com/p/gesture-recognition-toolkit/

##GRT Design Principles The toolkit is developed with the following core design principles:

• Accessibility: The GRT is a general-purpose tool for facilitating non-specialists to create their own machine-learning based systems. Emphasis is placed on ease of use, with a clear and consistent coding convention applied throughout the toolkit.
• Flexibility: To support flexibility while maintaining consistency, the GRT uses an object-oriented modular architecture. This architecture is built around a set of core modules and a central gesture recognition pipeline. The input to both the modules and pipeline consists of an N-dimensional double precision vector, making the toolkit flexible to the type of input signal.
• Choice: To date, there is no single machine-learning algorithm that can be used to recognize all gestures. It is therefore crucial for a user to be able to choose from, and quickly experiment with, a number of algorithms to see which might work best for their particular task. The GRT features a broad range of machine-learning algorithms such as AdaBoost, Decision Trees, Dynamic Time Warping, Hidden Markov Models, K-Nearest Neighbor, Linear and Logistic Regression, Naive Bayes, Multilayer Perceptrons, Random Forests, Support Vector Machines and more. In addition to supporting a broad range of algorithms, the toolkit's architecture facilities a user to seamlessly switch between different algorithms with minimal modications to the users code.
• Supporting Infrastructure: Building sophisticated machine-learning based systems requires more than just a state-of-the-art classifier. In many real-world scenarios, the input to a classification algorithm needs to first be preprocessed and have salient features extracted. The GRT therefore supports a wide range of pre/post processing, feature extraction and feature selection algorithms, including popular preprocessing filters (e.g. Moving Average Filter), embedded feature extraction algorithms (e.g. AdaBoost), dimensionality reduction techniques (e.g. Principle Component Analysis), and unsupervised quantizers (e.g. K-Means Quantizer, Self Organizing Map Quantizer). Accurate labeling of datasets is also critical to building robust machine-learning based systems. The toolkit therefore contains extensive support for recording, labeling and managing supervised and unsupervised datasets for classification, regression and time-series analysis.
• Customizability: In addition to supporting non-specialists, the GRT is designed to enable more advanced users to precisely customize their own recognition systems. The toolkit's modular design also facilitates an advanced user to incorporate their own algorithms within the GRT framework by wrapping their custom algorithm within a class that inherits from one of the GRT base classes.
• Realtime Support: The GRT supports common techniques for performing offline analysis on pre-recorded datasets, such as partitioning data into validation and test datasets, running cross validation, computing accuracy metrics, etc.. In addition to these offline techniques, the toolkit is designed to enable a user to seamlessly move from the offline analysis phase to the real-time recognition phase.

##GRT Architecture To support flexibility while maintaining consistency, the GRT uses an object-oriented modular architecture. This architecture is built around a set of core modules and a central gesture recognition pipeline.

The input to both the modules and pipeline consists of an N-dimensional double-precision vector, making the toolkit flexible to the type of input signal. The algorithms in each module can be used as standalone classes; alternatively a pipeline can be used to chain modules together to create a more sophisticated gesture-recognition system. The GRT includes modules for preprocessing, feature extraction, clustering, classification, regression and post processing.

The toolkit's source code is structured as following:

• ClassificationModules: Contains all the GRT classification algorithms, such as AdaBoost, Naive Bayes, K-Nearest Neighbor, Support Vector Machines, and more.
• ClusteringModules: Contains all the GRT clustering algorithms, including K-Means, Gaussian Mixture Models and Self-Organizing Maps.
• ContextModules: Contains all the GRT context modules, these are modules that can be connected to a gesture recognition pipeline to input additional context to a real-time classification system.
• CoreAlgorithms: Contains a number of algorithms that are used across the GRT, such as Particle Filters, Principal Component Analysis and Restricted Boltzmann Machines.
• CoreModules: Contains all the GRT base classes, such as MLBase, Classifier, FeatureExtraction, etc..
• DataStructures: Contains all the GRT classes for recording, saving and loading datasets.
• FeatureExtractionModules: Contains all the GRT feature extraction modules. These include FFT, Quantizers and TimeDomainFeatures.
• PostProcessingModules: Contains all the GRT post processing modules, including ClassLabelFilter and ClassLabelTimeoutFilter.
• PreProcessingModules: Contains all the GRT pre processing modules, including LowPassFilter, HighPassFilter, DeadZone, and many more.
• RegressionModules: Contains all the GRT regression modules, such as MLP Neural Networks, Linear Regression, and Logistic Regression.
• Util: Contains a wide range of supporting classes, such as Logging, Util, TimeStamp, Random and Matrix.

##Getting Started Example This example demonstrates a few key components of the GRT, such as:

• how to generate and save a basic labeled dataset
• how to save/load this data to/from a CSV file
• how to split a dataset into a training and test partition
• how to setup a new Gesture Recognition Pipeline and add a classification algorithm to the pipeline
• how to use a training dataset to train a new classification model
• how to save/load a trained pipeline to/from a file
• how to use a test dataset to test the accuracy of a classification model
• how to print the test results

You can find this source code and a large number of other examples and tutorials in the GRT examples folder.

//Include the main GRT header
#include "GRT.h"
using namespace GRT;

bool generateDataset( const string filename, const UINT numSamples, const UINT numClasses, const UINT numDimensions  );

int main (int argc, const char * argv[])
{
    //Load some training data from a file
    ClassificationData trainingData;

    //Generate a basic dataset with 1000 samples, 5 classes, and 3 dimensions
    cout << "Generating dataset..." << endl;
    generateDataset( "data.csv", 1000, 5, 3 );

    cout << "Loading dataset..." << endl;
    if( !trainingData.load( "data.csv" ) ){
            cout << "ERROR: Failed to load training data from file\n";
            return EXIT_FAILURE;
    }

    cout << "Data Loaded" << endl;

    //Print out some stats about the training data
    trainingData.printStats();

    //Partition the training data into a training dataset and a test dataset. 80 means that 80%
    //of the data will be used for the training data and 20% will be returned as the test dataset
    cout << "Splitting data into training/test split..." << endl;
    ClassificationData testData = trainingData.partition(80);

    //Create a new Gesture Recognition Pipeline using an Adaptive Naive Bayes Classifier
    GestureRecognitionPipeline pipeline;
    pipeline.setClassifier( ANBC() );

    //Train the pipeline using the training data
    cout << "Training model..." << endl;
    if( !pipeline.train( trainingData ) ){
        cout << "ERROR: Failed to train the pipeline!\n";
        return EXIT_FAILURE;
    }

    //Save the pipeline to a file
    if( !pipeline.save( "HelloWorldPipeline" ) ){
        cout << "ERROR: Failed to save the pipeline!\n";
        return EXIT_FAILURE;
    }

    //Load the pipeline from a file
    if( !pipeline.load( "HelloWorldPipeline" ) ){
        cout << "ERROR: Failed to load the pipeline!\n";
        return EXIT_FAILURE;
    }

    //Test the pipeline using the test data
    cout << "Testing model..." << endl;
    if( !pipeline.test( testData ) ){
        cout << "ERROR: Failed to test the pipeline!\n";
        return EXIT_FAILURE;
    }

    //Print some stats about the testing
    cout << "Test Accuracy: " << pipeline.getTestAccuracy() << endl;

    cout << "Precision: ";
    for(UINT k=0; k<pipeline.getNumClassesInModel(); k++){
        UINT classLabel = pipeline.getClassLabels()[k];
        cout << "\t" << pipeline.getTestPrecision(classLabel);
    }cout << endl;

    cout << "Recall: ";
    for(UINT k=0; k<pipeline.getNumClassesInModel(); k++){
         UINT classLabel = pipeline.getClassLabels()[k];
         cout << "\t" << pipeline.getTestRecall(classLabel);
    }cout << endl;

    cout << "FMeasure: ";
    for(UINT k=0; k<pipeline.getNumClassesInModel(); k++){
        UINT classLabel = pipeline.getClassLabels()[k];
        cout << "\t" << pipeline.getTestFMeasure(classLabel);
    }cout << endl;

    MatrixDouble confusionMatrix = pipeline.getTestConfusionMatrix();
    cout << "ConfusionMatrix: \n";
    for(UINT i=0; i<confusionMatrix.getNumRows(); i++){
        for(UINT j=0; j<confusionMatrix.getNumCols(); j++){
            cout << confusionMatrix[i][j] << "\t";
        }cout << endl;
    }

    return EXIT_SUCCESS;
}

bool generateDataset( const string filename, const UINT numSamples, const UINT numClasses, const UINT numDimensions ){

    Random random;

    //Generate a simple model that will be used to generate the main dataset
    MatrixDouble model(numClasses,numDimensions);
    for(UINT k=0; k<numClasses; k++){
        for(UINT j=0; j<numDimensions; j++){
            model[k][j] = random.getRandomNumberUniform(-10,10);
        }
    }

    //Use the model above to generate the main dataset
    ClassificationData data;
    data.setNumDimensions( numDimensions );

    for(UINT i=0; i<numSamples; i++){
    
        //Randomly select which class this sample belongs to
        UINT k = random.getRandomNumberInt( 0, numClasses );
    
        //Generate a sample using the model (+ some Gaussian noise)
        vector< double > sample( numDimensions );
        for(UINT j=0; j<numDimensions; j++){
            sample[j] = model[k][j] + random.getRandomNumberGauss(0,1);
        }
    
        //By default in the GRT, the class label should not be 0, so add 1
        UINT classLabel = k + 1;
    
         //Add the labeled sample to the dataset
         data.addSample( classLabel, sample );
    }

    //Save the dataset to a CSV file
    return data.save( filename );
}

##Tutorials and Examples

You can find a large number of tutorials and examples in the examples folder. You can also find a wide range of examples and references on the main GRT wiki:

http://www.nickgillian.com/wiki/pmwiki.php?n=GRT.GestureRecognitionToolkit

##Forum

You can find the main GRT forum at: http://www.nickgillian.com/forum/

##Building the GRT

You can find a CMakeLists file in the build folder that you can use to autogenerate a makefile for your machine.

Read the readme file in the build folder to see how to build the GRT as a static library for Linux, OS X, or Windows.

##Installing and using the GRT in your C++ projects

To use the GRT you simply need to add all of the code contained in the GRT folder to your c++ project.
If you are using an IDE such as XCode or VisualStudio then you can either add the files in the GRT folder to your project using "File -> Add Files to project", or in most IDEs you can simply drag the GRT folder (from Finder or Windows Explorer) into your IDE which should add all of the files in the GRT folder to your project.

After you have added the code in the GRT folder to your project, you can now use any of the GRT functions or classes by adding the following two lines of code to the top of any header file in your project in which you want to use the GRT code:

#include "GRT/GRT.h"
using namespace GRT;

The first line of code above adds the main GRT header file (GRT.h) to your project, this header file contains all of the GRT module header files which means you do not need to manually include any other GRT header files. The second line of code states that you are using the GRT namespace, this means that you don't have to write GRT::WhatEverClass every time you want to use a GRT class and can instead simply write WhatEverClass.

Note that, depending on your IDE, you may have to specify the physical path that you placed the GRT folder in on your hard drive, for instance you might have to write something like:

#include "/Users/Nick/Documents/gesture-recognition-toolkit/GRT/GRT.h"

##License

The Gesture Recognition Toolkit is available under a MIT license.

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.