Networked Objects Project 1
Physical Computing Improv Project:
My action that I've decided on is tapping:

And my object is pudding:
The end goal here, is to have a kinetic motion (pudding jiggling) to happen when a sensor picks up tapping.
As a sensor I picked an accelerometer, and as an actuator, I picked vibration motors.
The circuit I ended up with looks like this:
The pictures of the final product are here:
And with pudding intact:
One vibration motor didn't give enough jiggle, so I used two in parallel:
The sensor was noisy, and my algorithm was (where X = X, Y, and Z axes):
Take Xavg = average sensor reading (20 samples)
Xdiff = abs(Xavg - Xreading)
If Xdiff > Threshold, buzz, else re-run.
The problem with the algorithm, was that as the sensor drifted (as it liked to do), the pudding would start jiggling without reason.
I think if I were to do this project again, a better algorithm would be to use the math.h library (it has square root), take a running average of the inputs, and buzz only for any input that's over one standard deviation above the mean.

And my object is pudding:
The end goal here, is to have a kinetic motion (pudding jiggling) to happen when a sensor picks up tapping.
As a sensor I picked an accelerometer, and as an actuator, I picked vibration motors.
The circuit I ended up with looks like this:
The pictures of the final product are here:
And with pudding intact:
One vibration motor didn't give enough jiggle, so I used two in parallel:
The sensor was noisy, and my algorithm was (where X = X, Y, and Z axes):
Take Xavg = average sensor reading (20 samples)
Xdiff = abs(Xavg - Xreading)
If Xdiff > Threshold, buzz, else re-run.
The problem with the algorithm, was that as the sensor drifted (as it liked to do), the pudding would start jiggling without reason.
I think if I were to do this project again, a better algorithm would be to use the math.h library (it has square root), take a running average of the inputs, and buzz only for any input that's over one standard deviation above the mean.
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