Detecting Handedness from Device Use
You can definitely do this but if it were me, I'd try a less complicated approach. First you need to recognize that not any specific approach will yield 100% accurate results - they will be guesses but hopefully highly probable ones. With that said, I'd explore the simple-to-capture data points of basic touch events. You can leverage these data points and pull x/y axis on start/end touch:
touchStart: Triggers when the user makes contact with the touch surface and creates a touch point inside the element the event is bound to.
touchEnd: Triggers when the user removes a touch point from the surface.
Here's one way to do it - it could be reasoned that if a user is left handed, they will use their left thumb to scroll up/down on the page. Now, based on the way the thumb rotates, swiping up will naturally cause the arch of the swipe to move outwards. In the case of touch events, if the touchStart X is greater than touchEnd X, you could deduce they are left handed. The opposite could be true with a right handed person - for a swipe up, if the touchStart X is less than touchEnd X, you could deduce they are right handed. See here:
Here's one reference on getting started with touch events. Good luck!
http://www.javascriptkit.com/javatutors/touchevents.shtml
There are multiple approaches and papers discussing this topic. However, most of them are written between 2012-2016. After doing some research myself I came across a fairly new article that makes use of deep learning. What sparked my interest is the fact that they do not rely on a swipe direction, speed or position but rather on the capacitive image each finger creates during a touch.
Highly recommend reading the full paper: http://huyle.de/wp-content/papercite-data/pdf/le2019investigating.pdf
Whats even better, the data set together with Python 3.6 scripts to preprocess the data as well as train and test the model described in the paper are released under the MIT license. They also provide the trained models and the software to run the models on Android.
Git repo: https://github.com/interactionlab/CapFingerId