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HackPrinceton2015F -- Extends the Myo API to allow for custom gesture detection.

Myo Gesture extends the Myo API to provide support for user-defined custom gestures. A developer enters training data for a certain gesture by running the CollectRaw executable. He/she then runs the ProcessRaw executable to train the model for the gestures inputted.

The CollectRaw executable will create .rawmyo files in the .\rawdata folder, and the ProcessRaw executable will train a classifier on those .rawmyo files to create a GestureTrainingDataLabels.txt file and a DTWModel.txt file in the .\processed directory. These two filenames are passed to a GestureDeviceListener to initialize the correct training model.

Once this simple procedure has been followed to define custom gestures, it is easy to use the API. Simply include gesturedevicelistener.h, and let your device listener class extend GestureDeviceListener, instead of extending myo::DeviceListener. Your device listener class should work just as if it were extending GestureDeviceListener except that (1), whenever you call hub.run(int) for a Myo Hub hub, you should also call collector.recData() immediately afterward (where collector is your instance of your device listener class), and (2) when you override GestureDeviceListener's methods, override the methods with a 2 at the end of the name (e.g., void onAccelerometerData2(myo::Myo* myo, uint64_t timestamp, const myo::Vector3<float>& acceleration) { } instead of void onAccelerometerData(myo::Myo* myo, uint64_t timestamp, const myo::Vector3<float>& acceleration) { }.

This repo contains the source for Nick Gillian's wonderful GRT library, which should be built with the collectraw.cpp and gesturemyo.h files. Alternatively, the library may be found on Nick's webpage, at http://www.nickgillian.com/software/grt.

Also, in order to run the files in the Myo-Gesture repository, you need to have Myo Connect installed and the Myo device synced.