Choosing correct filter parameters for IMU sensor datas
1) HPF I would say you probably can't have one.
The  accelerometer has similiar considerations.

The gyroscope stream is in terms of xxx output=yyy angular velocity.  Any HPF analog or digital would sag to zero when you attach it to the hub of bicycle wheel or hub of a motor. 

The alternative to a HPF is "sample and hold" a value when the unit is at rest.  There are three alternatives.

1-a) During manufacturing take a string of readings at rest and store the value as an offset.

1-b) During power on do the same thing and tell the operator not to disturb it until a ready signal/light comes on.

1-c) Just live with the error of about 1/2 deg/sec ; or 1 rpm after 12 minutes.   That's as I read the error from your data.

If you are willing to live with the sag then I can do the calculations; but I keep thinking of the motor, or bicyclist, going faster and faster trying to keep up to the "speed".

An "alternative" is to differentiate the input and then integrate but that usually doesn't work out very well due to errors introduced during differentiation.

2) LPF

I got good results with a cutoff frequency .34 hertz and a fourth order bessel filter.  My octave 4 (a matlab like program) program and data are at:

https://www.dropbox.com/sh/av5w0qi4myc2tbm/AACxJllMNDsxT2x9nOvjFjMPa?dl=0
Here are the graphs for the first column of gyros.

)2-a Overlay of filtered output versus raw.


)2-b  Filtered signal

)2-c  Normal probability plot pplot()  Straight lines indicate that the errors are gaussian in distribution.  The lapse in the center is a deadband due to resolution


There are other tests but these exemplify the process.  I always check fittings with pplot (or relatives) to see if the fitting leaves gaussian in distribution.  The "staightness" fits into our visual system and is easy.  If not I want to know why; if so I want to know why :) The point is that for some (most?) process trying to dig into gaussian residues is a thankless task.  The truth is I would also look at the autocorrelation as well; but I doubt if you are interested.  One other thing is that one can also plot expected bounds on "straightness" if you want.  In fact this graph indicates that perhaps the filtering is excessive.