How to evaluate the learned prototypes for multivariate time-series (e.g. motion)? Consider a method which finds prototypes for multivariate time-series (MTS) data, and is designed to find prototypes for each class of data. 
For example, the {walking} class consists of some slightly different types of {walking} sub-classes, but they are not annotated. So i want to check if my prototypes can adequately represent the variations within each class and to measure the quality of the estimated prototypes.

In fact, I can calculate the distance of the prototype to the data points, but since it is possible to have more than one prototype per class/cluster, i cannot decide to which data point i should calculate this distance.

If the data is related to motion data, it is possible to watch the resulted prototypes to check how sensible they are. However, i'm not sure how to measure their quality in a numerical way?
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