This week we discussed about our adjustments to the project as we all are required to work remotely because of the COVID-19 situation. Although the fall detection algorithm can be developed as planned, integration with the hardware component will be hard. To account for this situation, we decided to develop each component of the project separately. My goal is to develop a fall detection algorithm using SVM, but it will not be real time as planned before. Instead of using real time data from the RPi device as the input, I would have to use pre-collected data stream to simulate the real time inputs. A possible solution would be to set up a client-server connection using Python’s socket library, and make the client send data to the server at the same time interval as the collected data. The server can then run the classifier to detect falls. For demonstration, graphing the data stream and notifying when a fall is detected could show that the algorithm correctly detects falls.
My work is currently behind schedule because of the change of plans for the project. Next week, I will continue collecting data for the SVM using my phone. It would be best to collect data with our RPi device, but since the IMU sensors on RPi will provide more accurate data, I predict that the classifier will work as expected on the RPi if it works with the data collected from my phone. I will also have to come up with different ways to use phase data for classification as there was a concern about it being different depending on the orientation of the device. In addition to that, I will have to figure out how to use data stream as an input to the SVM, as the current version only supports importing data from csv files.