Classifier for time based data to binary label
Yes it definitely is feasible and also very common. Search for any document classification tasks (e.g. sentiment) for examples of this kind of tasks.
I have access to a dataframe of 100 persons and how they performed on a certain motion test. This frame contains about 25,000 rows per person since the performance of this person is kept track of (approximately) each centisecond (10^-2). We want to use this data to predict a binary y-label, that is to say, if someone has a motor problem or not. 
Trained neural networks on mean's and variances of certain columns per person classified +-72% of the data correctly. 
Naive bayes classifier on mean's and variances of certain columns per person classified +-80% correctly.
Now since this is time based data, 'performance on this test through time', we were suggested to use Recurrent Neural Networks. I've looked into this and I find that this is mostly used to predict future events, i.e. the events happening in the next centiseconds. 
Question is, is it in general feasible to use RNN's on (in a way time-based) data like this to predict a binary label? If not, what is?