Electrical Impedance Tomography (EIT) was recently employed in the HCI domain to detect hand gestures using an instrumented smartwatch. This prior work demonstrated great promise for non-invasive, high accuracy recognition of gestures for interactive control. We introduce a new system that offers improved sampling speed and resolution. In turn, this enables superior interior reconstruction and gesture recognition. More importantly, we use our new system as a vehicle for experimentation -- we compare two EIT sensing methods and three different electrode resolutions. Results from in-depth empirical evaluations and a user study shed light on the future feasibility of EIT for sensing human input.
Yang Zhang, Robert Xiao, and Chris Harrison. 2016. Advancing Hand Gesture Recognition with High Resolution Electrical Impedance Tomography. In Proceedings of the 29th Annual Symposium on User Interface Software and Technology (UIST '16). ACM, New York, NY, USA, 843-850. DOI: http://dx.doi.org/10.1145/2984511.2984574