MYO Armband Machine Learning gesture classifier

Myo Armband Gesture classifier with Machine Learning. I used Fastai for simplicity and converted EMG signals from MYO to pictures and then proccessed with a Convolutional Neural Network to classify between gestures.

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Usage:

First of all install Thalmic Labs Myo Controller.
Then, if you want to train with your own gestures (recomended), add the gestures name to the file gestures.txt (comma separated).

Run gesture_data_recorder.py and follow the steps to record the dataset of gestures, you can change the number of training records for gesture just changing num_rcdings (recomended more than 30, you can also duplicate images but not recomended).

Then run all the cells from trainer.ipynb. If you want to display pictures of the gestures once recognised add them to the folder gest_images and change the last cell from trainer.ipynb.

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Files:

• gesture_recorder.py: Contains the function record(file,secs), it saves an image of the plot of the last 3 seconds of an interval of secs seconds with name file.jpg .
• gesture_data_recorder.py: Do the process of recording data to the dataset for every gesture.
• train.ipynb trains the CNN and in the last cell can classify gestures (later this will be in other file and will run in real time).

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Self recomendations for the future:

• Do not use a CNN to classify gestures, they work, but probably not the best solution.
• Create an app to clasify your own gestures in real time, it can be done now but not in real time.

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This proyect uses Niklas Rosenstein 's Myo-Python library, a great library to manage MYO with python.

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This project is made with love to learn and contribute to the Myo community.

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2021 Leonardo Artiles Montero