Team E7: Body Buddy

This week I worked on adding new features to the SVM to further increase its accuracy. I tried adding the variance of the angles and magnitudes. Because the variance indicates how spread out the data is, I thought it would be helpful in characterizing the falls as the magnitudes and angles will deviate a lot more from their mean for falls than non-fall activities. Adding variance as a new feature did not change the number of false negatives, but it reduced the number of false positives. I also wrote a program that randomly chooses a test data and returns the number of false positives and false negatives with the model that is trained without the test data. I tried running the program with different combinations of features to find the optimal features.

In addition to this, I worked on the final presentation slides, and prepared for the presentation as I will be presenting next week.

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.