How to evaluate the learned prototypes for multivariate time-series (e.g. motion)?
You note " but with more than one prototype per class/cluster."

We often call this a polymorphic class. Consider the analogue in text..

C1 = { dpacekfjklwalkflwalkklpacedalyutekwalksfj}
 C2 = { jhjhleapashljumpokdjklleaphfleapfjjumpacgd}

Here the class 1 prototypes are polymorphic {walk OR pace}, and so is class 2 {leap or jump}.

You can learn these prototypes with a dictionary building method, and "measure their quality in a numerical way" with just classic accuracy (or precision recall etc).

Using nearest neighbor, the distance between and unknown item, say 'balk' is just defined as:

dist('balk', C1) = min(dist('balk','walk'),dist('balk','pace'))
and
dist('balk', C2) = min(dist('balk','leap'),dist('balk','jump'))

Paper [a] explains one nice dictionary building method, but there are other ways.

[a] http://www.cs.ucr.edu/~eamonn/SDM_RealisticTSClassifcation_cameraReady.pdf