Sojeong’s Status Update for 02/15/2020

This week, I spent time reading existing research papers about different fall detection algorithms. The main points that I was looking for in those papers were what kinds of data they collected to train their algorithm and how they tested it. One of the papers only used accelerations in three axes to train their algorithm, and got 99.14% accuracy for their best results. This was surprising because I thought we would need more than just accelerations to build an accurate algorithm. Seeing the results, we decided to start our project by collecting acceleration data first, and if the results are not satisfying enough, we will combine other data such as angular velocity. For testing, I came up with different types of activities for fall and non-fall categories to clearly define what falls are and to narrow down the types of activities that the algorithm is going to detect. For the next week, we will get the accelerometer and Raspberry Pi, so I am planning to get some data and start writing the fall detection algorithm using SVM. Because the accelerometer data might not be accurate and include some noises, I would also have to think about some ways to preprocess the data.