I am working on a project which aims at determining whether a patient has cervical issues or not, based on a certain movement (for instance, turning the head from left to right and so on).
For each patient, I have one or more sets of coordinates acquired with a VR headset. One problem is, all the patients are assumed healthy and I cannot compare their data with patients who actually have cervical issues.
I am currently working with two coordinates at a time, not the three, and I am considering two approaches: the first is to use approximation (splines...), the second is to use concave hulls. I am a bit more inclined to use hulls and I thought that I could calculate the distance between two hulls A and B as follows: area(A\B)+area(B\A). Note that the curves are parametric.
I have two questions:
- Is it possible to "classify" patients using a data set that only consists of healthy patients? Or to find a "descriptor" for them ?
- If so, or assuming I can get data on unhealthy patients, what tools can I use to classify the curves? I did not find anybody who worked on a similar problem.