Last weekend @devdevcharlie posted a gesture tracking machine learning demo to enhance the Street Fighter experience. The DIY version of the project starts by building a gesture tracking device using some simple hardware. Specifically, an Arduino MKR1000, a MPU6050 accelerometer/gyroscope and a button. Checkout the fritzing diagram below for the setup.
The tutorial uses the accelerometer to capture body movement for a training data set. Instead of tracking all movement, the button was used to start recording during relevant motion. Once gesture data for a punch, uppercut and hadoken were captured it was cleaned for use in a TensorFlow.js neural net classification model. The resulting model is able to distinguish between the three gestures (punch, uppercut, hadoken). The end result is a pretty impressive demo and a website that lets you test out your moves with a smartphone. If you’d like to try implementing the code yourself you can checkout @devdevcharlie’s tutorial or GitHub repo.
Eink, E-paper, Think Ink – Collin shares six segments pondering the unusual low-power display technology that somehow still seems a bit sci-fi – http://adafruit.com/thinkink
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