One of the most common applications of EMG signals in the research field is to use them as inputs of machine learning algorithms to classify gestures performed with the hand. These classifiers are very useful to control robotic hand prostheses with more complex controllers than the usual threshold-based on-off controllers. But they also have an application in the recreational field: they can be used as part of a hands-free control system for computers, video games or mobile phones.

As a tutorial/application example of how such controllers can be implemented, and as part of the Citizen Scientist challenge, I created a Jupyter Notebook with a step by step explanation covering the different aspects about one possible implementation that relies on Python and its scikit-learn library.

Implementation of an EMG-based hand gesture classifier