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.