This week, I worked on calculating the accuracy of the fall detection algorithm for different combinations of features. These values will help us choose the best feature combination to use, and we will also include these in our final paper to compare different features that we tried. To calculate the accuracy, I used one csv file from our data set and trained the SVM with the remaining files, and repeated this for all the files. The accuracy is calculated by dividing the number of true positives and true negatives by the total number of trials. Below is a table of the accuracy for each feature combination.
|
magnitude, change in angle (x, y, z) |
85.976 |
|
magnitude, change in z-angle |
96.895 |
|
magnitude |
93.651 |
|
change in z-angle |
23.034 |
Although the accuracy of the algorithm with magnitude and change in z-angle as features is high, there were number of false negatives, so I will look into adding new features that could reduce this number. I will also continue the integration with the RPi next week.
In terms of schedule, we are on time.