Project:
Atlantis

Building a signal classifier (for a water-touch-interface)

Context

Elective: Creating Rich User Experiences with Applied Machine Learning
Student Project

Team

Maximilian Brandl
Philipp Kaltofen

Supervised by
Meghan Kane (@meghafon)

Buzzwords

Machine Learning, Tangible Interfaces

Machine learning is a great technology for classifying and interpreting huge amounts of data. One of the most interesting parts of this technology is the use of data that is not visible to the human eye.

There is much more in the world than we actively perceive. We are surrounded by many different invisible activities that can be measured in frequencies. Our goal was to create a general signal classifier that can be trained on all these different frequencies and interpret a generalized data stream.

As a test environment we wanted to build a water-touch-interface. The interface should be trained in real time to recognize if a finger, two fingers or the whole hand is in the water and thus control a Unity application.

IMG_1219

The sensor

A sensor to generate training data for the  signal classifier to turn objects into touch-sensitive interfaces. More infos about the sensor on Github. 

UnoTouche

The madness


IMG_1224
IMG_1277

The prototypes


ArduinoPlotter
UnoData
heat3