Team E7: Body Buddy

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.

This week I worked on adding new features and developing a visual interface for the fall detection. For the new features, instead of using the raw phase values, I tried using changes in phase. This way, the orientation of the device does not affect the phase values. After combining the change in phase with the magnitude, the algorithm now accurately categorizes jumping and running activities as non-falls. Also, I combined Jacob’s frequency features and tested the algorithm, but the frequency features did not improve the accuracy of the algorithm. Jacob will work on changing the frequency features so that they improve the accuracy of the algorithm.

The visual interface is implemented for the demo next week. Because we currently do not have our components integrated, I thought that it would be useful to visualize the fall detection to make it easier to demonstrate that the algorithm works. When the program runs, it shows the graph of the input acceleration data, and a rectangle with a size of our sliding window moves through the graph and displays the algorithm’s prediction for each window.

I also collected some fall and non-fall data to compare them with the dummy data that Jacob is collecting. From previous data collection, I held my phone on my hand but this time I placed my phone in my pocket to include noises and  possible orientation changes of the device in the pocket.

Next week, I will send the SVM program to Max and we will try integrating the fall detection and raspberry pi components. We will have to check how long it takes for the algorithm to run on the pi and improve the running time if it takes too long. If the algorithm takes too long to run, I will try reducing the feature size. Currently, the size of the feature array for each sliding window is 20: 10 for magnitudes and 10 for phase changes. If I only use the maximum values of each features, I can reduce the size to 2 per window, which will decrease the run time for a large train data.

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.