EIT-kit: An Electrical Impedance Tomography Toolkit for Health and Motion Sensing

EIT-kit: An Electrical Impedance Tomography Toolkit for Health and Motion Sensing Junyi Zhu, Jackson C Snowden, Joshua Verdejo, Emily Chen, Paul Zhang, Hamid Ghaednia, Joseph H Schwab, Stefanie Mueller UIST'21: ACM Symposium on User Interface Software and Technology Session: Motion Tracking Abstract In this paper, we propose EIT-kit, an electrical impedance tomography toolkit for designing and fabricating health and motion sensing devices. EIT-kit contains (1) an extension to a 3D editor for personalizing the form factor of electrode arrays and electrode distribution, (2) a customized EIT sensing motherboard for performing the measurements, (3) a microcontroller library that automates signal calibration and facilitates data collection, and (4) an image reconstruction library for mobile devices for interpolating and visualizing the measured data. Together, these EIT-kit components allow for applications that require 2- or 4-terminal setups, up to 64 electrodes, and single or multiple (up to four) electrode arrays simultaneously. We motivate the design of each component of EIT-kit with a formative study, and conduct a technical evaluation of the data fidelity of our EIT measurements. We demonstrate the design space that EIT-kit enables by showing various applications in health as well as motion sensing and control. DOI:: https://doi.org/10.1145/3472749.3474758 WEB:: https://uist.acm.org/uist2021/ Full Videos of the UIST 2021 Papers Program