Building a signal classifier (for a water-touch-interface)
Elective: Creating Rich User Experiences with Applied Machine Learning
Meghan Kane (@meghafon)
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.