I have a data set which measures 60 data points in a second (60Hz). Clearly, I do not really need all 60 points in one second since this only generates some noise.
ABOUT MY DATA: So my data sets are primarily location/position data (x,y) coordinates that are obtained from a motion tracker. The motion tracker measurements are expected to have some noise in them (but nothing out of the ordinary). If I zoomed out of the graphs (to see the bigger picture, I am satisfied with the graphs). Zooming into time $t$ and $t+\delta$ would mean you are staring at the zigzag lines that may be attributed to errors. In the end, I would like to compute velocities, speeds, etc, without having to worry about the errors.
What are some examples of filtering techniques that would be applicable to this? I am pretty sure this would depend on the circumstances.
Thanks for your insights.