This is a video with a small demo for the NNiimote.
NNiimote is just a small proof of concept appplication that uses a neural network to recognize Wiimote gestures. The neural network was implemented in R (I didn't actually implement the net, I used the AMORE library) and the "driver" program was made in Python. The Wiimote data is feed to the Python program via OSC from GlovePIE and the Python program invokes the net via the Rpy package.
In the video you can see it recognizing the three moves that the network was trained for:
One movement is "delimited" by pressing the A button on the Wiimote.
I used the acceleration data (X, Y, Z) from a set of sequential readings of a 'gesture'. A gesture was initiated and terminated by pressing a button. The set was condensed to 150 interpolated values (3*50) and fed to the NN.
But NNiimote was just an exercise... I think that a gesture recognition using regular expression (http://silentlycrashing.net/ezgestures/) is probably better for simple gestures...
I think those "train.moves" aren't used... It's probably some leftover from my experiences.
The 'Negative-moves' are moves diferent from all the rest. (An NN is probably not the best way to recognize moves because there is allways an extra class of moves which is impossible to define: my negative-moves are just an attempt to define them...)
The train files are generated by the niimote-save-moves.py file. You should train the yes, no, attack and some other moves (negative).