I am currently looking to use a neural network to classify gestures. I have a series of Dx,Dy,Dz readings that represent the differences across the three axes made during the gesture. About 10 movements for each example of the gesture. Basically a 10x3 matrix and then classify the training data into about 15 classes. I plan to use a CNN classifier to do this because, while the time domain is relevant this problem the difference in the movements can be differentiated when presented with as a discrete matrix.
I'm used to using images with a neural net so I instinctively want to just convert the matrices into a 2D tensor and feed them into a CNN, but I was wondering if there was a better way to do this? For example, I have seen 1D tensors passed to a fully connected neural network for classification which seems like it could be more appropriate for this data input type?
Any tips on general architecture would be really appreciated as well!