Month: May 2019

For this week, I worked on readying up the stethoscope for the final demo. We all worked together on the final integration portion. We discovered an issue where the sample rate that the Raspberry Pi was recorded in was different from the sample rate that the testing data was. The frequency sample rate was 2000 Hz for the testing data, but the recorded data from the stethoscope was 44.1 kHz. Therefore, once we fixed this issue, importing the audio issue was now in the same format as the dataset. We also had issues where the recorded samples were at different amplitudes than the dataset sound files. Therefore, we added a gain setting on the Raspberry Pi, that amplifies the speaker/heart sound when recording on the stethoscope, so that the amplitudes would match. After fixing both of these, the testing samples on the speaker setup worked well. We also had to hand select some files from the testing dataset, so that we would ensure that it would work for the final demonstration. This is the final week so there are no updates for next week.

This week was one where we got the most accomplished. We were able to integrate our system and get it running on the Pi with processing being done remotely on the ECE machine in the lab. We were able to get the visualization and the recording working on the pi, however we ended up destroying the sound card on the pi which then caused the touch screen to stop working. As a result, for the demo we will be doing it all on my chromebook. Further, we were able to confirm our testing setup mimics the heart and were able to successfully classify 93% of the heart sounds we tested on which was much better than what we expected it to be.

• What did you personally accomplish this week on the project? Give files or photos that demonstrate your progress. Prove to the reader that you put sufficient effort into the project over the course of the week (12+ hours).
  • We all worked on integrating the whole stethoscope (front-end/back-end) so it accurately classified heart sounds through the stethoscope.
  • I worked on the final frontend GUI using Matplotlib and tkinter to display user’s heat sound signal for our raspberry pi.
• Is your progress on schedule or behind? If you are behind, what actions will be taken to catch up to the project schedule?
  • Our progress is on schedule
• What deliverables do you hope to complete in the next week?
  • Finish the project in time for the demo on Monday.

1. This week I continued with the integration and development of the  integrated system. I worked with using matplotlib to plot the incoming signal and applied a butterworth filter to filter out high freqency noise. I also helped out with the ML algo with Eri and Ryan to make progress a little faster. I also refactored the matlab code and did reasearch on improving our processing.