This week I worked on combining the magnitudes and angles of the accelerations as input features to the SVM. At first, I tried inputting them as tuples, but the SVM did not support inputs with dimensions greater than two. Instead of using them as tuples, I appended the angle values after the magnitudes. Now that I have three different combinations of input features (magnitudes, angles, magnitude & angles), I compared the accuracy of each of the features. I randomly chose a test data, then trained the model with the remaining data and counted the number of false positives and false negatives. When the angles were used, there were no false negatives but false positive rate was high and detected all normal activities as falls. This was unexpected because the graphs of the angles showed large differences between the falls and non-falls. However, because using magnitudes as the only feature still provides accurate results, the low accuracy of angle features is not a big problem.
Next week, I am planning to add a visual component to the fall detection algorithm so that it is easier to demonstrate that the algorithm works. I will write a program that graphs the inputs and shows when a fall is detected. I will also get dummy data from Jacob next week and make sure that the algorithm works accurately with his data.