This week I worked on refactoring the SVM code for integration with the Raspberry Pi. Previously, the graphical interface for the demo was not separated from the training and prediction portion of the code. I created a separate class for the SVM model and trained the model in the init function. I also moved the prediction to a separate function in the class so that the model only has to be trained once when it is initialized. Max ran the code on the Pi and it did not have any performance problems, so I did not have to make any adjustments to the size of the feature array. I also collected more data for non-fall activities, because I realized that most of the data that we have is for different types of falls.
Next week is the last week before the final presentation, so I will work on final improvements to the SVM. The integration with the pi seems to be working well, and it will not be that hard to import the modified code on the pi, so I will try adding one or more new features to the SVM and make final accuracy calculations.