In this project you will be using machine learning to try to predict arm movement from collected EMG signals.

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Project Overview

In this project you will be using machine learning to try to predict arm movement from collected EMG signals. Electromyography (EMG) measures electrical activity in response to a nerve's stimulation of the muscle. The dataset was collected from healthy individuals during movement of their arm. By being able to predict intended arm movement from brain signals, such data could be very useful in the prediction of the intended movement in bionic arms/ prosthesis for amputees.

The data set contains the following attributes:

Time: Timestamp measured in microseconds

Train X: The angle at the elbow of the arm at a given time (the main target attribute we want to predict)

Train v(x): The velocity of arm movement at a given time (optional target attribute). Train a(x): The acceleration of arm movement at a given time (optional target attribute).

Channel 1, 2, 3, and 4: These are the Electromyography signals (EMG) collected from 4 different muscle groups. This signal is the one responsible for contraction of these muscles, thus causing the arm to move. The signal carries information about the intended movement.

Your main goal will be to use machine learning techniques to predict the elbow angle using the EMG signals.

Attachments:

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