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I've been attempting to develop a means of synthesizing human-like mouse movement in an application of mine for the past few weeks. At the start I used simple techniques like polynomial and spline interpolation, however even with a little noise the result still failed to appear sufficiently human-like.

In an effort to remedy this issue, I've been researching into ways of applying machine learning algorithms on real human mouse movement biometrics in order to synthesize mouse movements by learning from recorded real human ones. Users would be compiling a profile of recorded movements that would trainh= the program for synthesis purposes.

I've been searching for a few weeks and read several articles on application of inverse biometrics in generating mouse dynamics, such as Inverse Biometrics for Mouse Dynamics; they tend to focus, however, on generating realistic time from randomly-generated dynamics, while I was hoping to generate a path from specifically A to B. Plus, I still need to actually need to come up with a path, not just a few dynamics measured from one.

Does anyone have a few pointers to help a noob?

Currently, testing is done by recording movements and having I and several other developers watch the playback. Ideally, the movement will be able to trick both an automatic biometric classifier, as well as a real, live, breathing Homo sapien, too.

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To record mouse movements, you need to store human movements. You can take a passing point A and ending point B. I can’t think of another way to do this.

Or, you can measure deviations from the direct path, it can be with respect to time.You need to take care that movements look different for different angles, path length etc.
 

Try a normalized model for this to get basic structure like rotations, time and space movements. 

If you successfully collect, these human mouse movements records, then you have to proceed with data preprocessing, model training and model evaluation.

Hope this answer helps.

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