A3: Bikers Friendly

 A3: Applications of Machine Learning models

Jianhao Ma

Response to PoseNet's Data & Model Biography

To be more specific the biography provided is for MobileNet. However, I did learn something about PoseNet because it's the sub model of MobileNet. 

One important thing I noticed is that the model is optimized for devices with limited performance, ones that can be easily embedded and require less power. Therefore, it can be applied to outdoor activities in daily life scenarios. Moreover, PoseNet does a really good job in inspecting and classifying people's poses and gestures. All of the followings inspired me to make my project this week.

Have A Safe Ride

My intention is to make a demo of an application that detects motorcycle hand signals. 
My plan for the application is to embed them in auto-driving and safety technologies on automobiles. It enables cars to recognize the hand signals of bikers in front of them and responses safely based on that.

I built my project on the PoseNet p5 sketch sample. Firstly, I ''console.log''ed the ''poses'' object to determine which variables I can use. Soon I found the ones I needed, coordinates of ''left shoulder'', ''left elbow'', and ''left wrist''. 

Then I just used these coordinates to measure the directions and distance of each part of left arm, which determined the hand signal. To give the video captured a measure that changes relatively to person's size, I set the meter equals to the distance bet ween two ears.
For the time I have, I just coded three signals for this demo, which are: left turn, right turn, and stop. The real challenges hasn't yet to come because those are static signals. I can imagine coding dynamic signals tedious.

p5 sketch: https://editor.p5js.org/roger1mjh/sketches/ssXmLw46-

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