Mouse movement data is a fairly potent fingerprinting vector. Bucketing the average spouse speed and acceleration rates could provide provide useful information. This may imply specific OS speed settings, or physical mouse DPI. A machine learning system would likely be able to distinguish traditional mouse, vs trackpoint, vs touchpad, vs trackball. Etc.

Also it is not just bots that have non-human like mouse movement. Many assistive technologies would have no mouse movement, or would auto snap the mouse to relevant spot. That is actually a quite powerful for fingerprinting, since assistive technology users are a pretty small subset of internet users, so only a relatively small amount of additional data is needed to uniquely fingerprint that user/machine.