Answer Happy

However, users typically utilize the PPG sensors on their wrists when exercising. Since the PPG sensor is not tightly attached to the wrist, during exercise, the PPG signal gets corrupted with moving artifacts. Assume that the PPG signal has been subjected to a noise due to the user's movement. The noisy PPG signal is saved in a MATLAB data file named myppgtype1.mat (download the file and use load ('myppgtypel.mat') on your code to get the data). You will have two variables: ppgsig and t. From the t variable obtain the sampling frequency fs. Plot the noisy PPG signal versus time. Observing the noisy signal, you will see that it will not be easy to use the time domain method to obtain beats per minute. However, you can with some signal manipulation and the FFT. Use the mat file, uploaded on this exam site, to accomplish the following tasks: You may recall from the second assignment that there was a rather large DC component as well as higher harmonics in the ecg signal. Similarly with the PPG signal; hence, find the
noisy PPG signal mean and remove it from the original noisy PPG signal, i.e. your noisy PPG signal should have a zero mean before your proceed to the next step. Remove the noise and higher harmonics out of the signal with a low pass Butterworth filter¹, use a cut off frequency of about 4 Hz, and a filter order of about 4. You can play with other numbers if you wish. Use the FFT to determine the beast rate per minute by examining the spectrum of the signal. Plot the spectrum of the signal, after the low pass filter, with the horizontal axis clearly labeled beats per minute (BPM). Please note that BPM for persons during exercise may be greater than 100². Fix the horizontal axis from 0 BPM to about 200 BPM. Find the signal's appropriate BPM with data tips. Submit your exam, with your code and graphs, of the raw time domain signal before and after the filter. Find the spectrum of the PPG signal after the filter and have the FFT graphs depicting bpm. Include your observations and/or comments. Were you able to determine the BPM under noisy conditions?