As Inti Einhorn mentioned in his presentation (which was great, man--I've had several conversations based on it in just the past couple of days,) getting the Wii to work requires training. That'll be true of anything we build, too, especially w/r/t anything gestural.
With that in mind, I would strongly recommend that we look into neural networks as a way to train the machines. It sounds a little intimidating, but it really isn't. In fact, it ties in very closely to the reading about logic gates we've been doing so far.
IBM has a pretty good introduction to NN. In particular, read Threshold logic units (TLUs) and How a TLU Learns. The rest is pretty math heavy--though it's still good reading, if you're up for it. You can recreate AND, OR, XOR, etc. gates using neural networks pretty easily.
Hot on the heels of our readings in class are two unconventional examples of how logic gates work.
The trick for me, if you look at the photo, was to use a voltage divider for the photoresistor. That is, the analog input comes from the point on the circuit between the photoresistor and a 1K ohm resistor.
The Arduino coding session we had today seemed to go pretty well (though I'll let the other folks blog about what it was like on the student end of things.) We got a bunch of things blinking and buzzing, and we covered most of what you need to do to read and write digitally and analog... analogally... analogly... analogalogally... hmm, don't know the word for that. At any rate, it was fun for me.
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