HRM statistics that we don't have yet (and a summary of previous conversation about them)

There are the .filt values that is bandpass filtered but it has issues with clipping out I guess because they are all 16 bit. It also bandpasses the wrong frequencies at 25Hz I think? I might have a better approach for filtering from the raw signal that I've put a write up on in more detail here:

https://www.majorinput.co.uk/post/bangle-js-2-dealing-with-noisy-ppg-signals

Basically you take a sample of .raw data then:

1. apply differencing to remove low frequency noise (subract last value by current in each array element)
   
2. apply differencing again (I didn't mention it in the blog post but it works better doing it twice)
   
3. convolve with a gaussian filter.
   
   

With the above, you won't get clipping issues because the values reduce as it's filtered down and you get a much better fourier transform. You could derive the HR by FFT from this even with shorter windows or calculate by peak detection and then based on the average interval/gap in the window. I've got a javascript app that runs on the bangle alligned to this but step 3 in the above is running a bit slow, I'll try getting it to run faster or even try your test harness thing and see how it works at firmware level. To be honest I think the proprietary HR algorythm seems fine so wouldn't change it for HR but this could be useful for HRV if it works ok and replace the filter algorythm.