Capacitive sensing + Machine Learning, 2018
During a four weeks project on Play and Ludic Interaction, I have explored capacitive sensing as a starting point for the project. During the first week, my group and I have explored the qualities of different conductive materials when combined with capacitive sensing.
We have been working with the Tact library by NANDstudio, which is capable to capture very rich data from the sensor. Different materials and objects each affordance different interactions, and this also affects which spectrum readings can be read by the sensor. E.g. when a jar of water is used as a capacitive sensor it peaks when the water is touched. Where a bag of wet sand peaks when squished tightly.
Through these explorations, we have found especially two very interesting ways of using capacitive sensing. Firstly, It’s possible to create a “chain” of sensors that works through non-conductive materials (wood, glass, acrylic, etc.). E.g. we had a path of aluminum foil that could sense proximity and touch on a jar of water, through a 4mm layer of wood/acrylic.
And secondly, classifying the data with Machine learning (we used Wekinator) can be used to recognize different gestures very well. This is mainly due to the rich data from the Tact library. We used 32 inputs pr. Sensor.
These findings have been the core for our project in Play and Ludic Interaction, and the mechanics we developed can be seen on the gifs/video. Hopefully I can soon share how we have applied this technology in a concept.