Summary of Data Capture and Classification

Over the past few days I have worked on capturing data from our mbed and processing it through a python script and then using a tool in order to classify the data that we captured.

We started by using an accelerometer since we did not have our emg sensor yet.
Here is a matlab plot of the data we collected from the accelerometer. We were able to classify this data based only on the entropy of the signal.

After we got our emg sensor we began capturing data. We had to figure out where to put the electrodes. We decided that the side of the face would be the best location.

Here is an example of 5 seconds of talking followed by 5 seconds of chewing.

Once we found that we could reliably capture the EMG data. We then captured a full minute of resting and talking data and a full minute of chewing data. We then put it through a machine learning program and produced the following tree.

 

Our next step is to recreate the tree on the mbed and classify the data.