Ask HN: How to detect the three bursts in this data
I have this accelerometer data from my phone:
http://imgur.com/a/cJoJH
The first artifact is when I lifted my phone from the table, the 3 bursts in the middle when I tapped on my phone 3 times and the last artifact when I placed it on the table again.
I want to detect the 3 taps in the middle algorithmically and ignore the other things.
I already have some working code that relies on thresholds, and another version that looks for high peaks followed by smaller peaks within a window.
But I think there should be a more reliable solutions to solve this, I assume this is a somewhat common problem. I also played around with FFT and power spectrums a little bit but I am not really satisfied. Maybe I should train a neural network or another machine learning algorithm?
The first artifact is when I lifted my phone from the table, the 3 bursts in the middle when I tapped on my phone 3 times and the last artifact when I placed it on the table again.
I want to detect the 3 taps in the middle algorithmically and ignore the other things.
I already have some working code that relies on thresholds, and another version that looks for high peaks followed by smaller peaks within a window.
But I think there should be a more reliable solutions to solve this, I assume this is a somewhat common problem. I also played around with FFT and power spectrums a little bit but I am not really satisfied. Maybe I should train a neural network or another machine learning algorithm?
How would you solve that?