EIT-kit Demo: An Electrical Impedance Tomography Toolkit for Health and Motion Sensing
Authors: 
Junyi Zhu, 
Jackson C Snowden, Joshua Verdejo, Emily Chen, Paul Zhang, Hamid Ghaednia, Joseph H Schwab, 
Stefanie MuellerAuthors Info & Claims
Pages 100 - 102
Abstract
In this demo, we present 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 the electrode arrays and the electrode distribution, (2) a customized EIT sensing motherboard that users can use to perform measurements, (3) a microcontroller library that automates electrical impedance measurements, and (4) an image reconstruction library for mobile devices for interpolating and then visualizing the measured data. Together, these 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 demonstrate each element of EIT-kit, as well as the design space that EIT-kit enables by showing various applications in health sensing as well as motion sensing and control.
References
[1]
Andy Adler and William R B Lionheart. 2006. Uses and abuses of EIDORS: an extensible software base for EIT. Physiological Measurement 27, 5 (apr 2006), S25–S42. https://doi.org/10.1088/0967-3334/27/5/s03
[2]
John H. Arnold. 2004. Electrical impedance tomography: on the path to the Holy Grail. Crit Care Med (Mar 2004), 894–5. https://doi.org/10.1097/01.ccm.0000115261.88801.24
[3]
H. Ghaednia, T.N. Tallman, C.E. Owens, A.J. Hart, and K.M. Varadarajan. 2020. NON-INVASIVE DIAGNOSIS OF ASEPTIC IMPLANT LOOSENING VIA ELECTRICAL IMPEDANCE TOMOGRAPHY. Orthopaedic Proceedings 102-B, SUPP_1 (2020), 130–130. https://doi.org/10.1302/1358-992X.2020.1.130 arXiv:https://online.boneandjoint.org.uk/doi/pdf/10.1302/1358-992X.2020.1.130
[4]
R. Harikumar, R. Prabu, and S. Raghavan. 2013. Electrical Impedance Tomography (EIT) and Its Medical Applications: A Review. International Journal of Soft Computing and Engineering.
[5]
David Holder. 2005. Electrical Impedance Tomography : Methods, History and Applications. Vol. 32. Institute of Physics. https://doi.org/10.1118/1.1995712
[6]
Yo Kato, Toshiharu Mukai, Tomonori Hayakawa, and Tetsuyoshi Shibata. 2007. Tactile Sensor without Wire and Sensing Element in the Tactile Region Based on EIT Method. Proceedings of IEEE Sensors, 792 – 795. https://doi.org/10.1109/ICSENS.2007.4388519
[7]
MathWorks. [n.d.]. MATLAB. https://www.mathworks.com
[8]
M. Michalikova and M. Prauzek. 2014. A hybrid device for electrical impedance tomography and bioelectrical impedance spectroscopy measurement. In 2014 IEEE 27th Canadian Conference on Electrical and Computer Engineering (CCECE). 1–4. https://doi.org/10.1109/CCECE.2014.6900984
[9]
Munehiko Sato, Rohan S. Puri, Alex Olwal, Yosuke Ushigome, Lukas Franciszkiewicz, Deepak Chandra, Ivan Poupyrev, and Ramesh Raskar. 2017. Zensei: Embedded, Multi-Electrode Bioimpedance Sensing for Implicit, Ubiquitous User Recognition. Association for Computing Machinery, New York, NY, USA, 3972–3985. https://doi.org/10.1145/3025453.3025536
[10]
Yang Zhang and Chris Harrison. 2015. Tomo: Wearable, Low-Cost Electrical Impedance Tomography for Hand Gesture Recognition. In Proceedings of the 28th Annual ACM Symposium on User Interface Software & Technology (Charlotte, NC, USA) (UIST ’15). Association for Computing Machinery, New York, NY, USA, 167–173. https://doi.org/10.1145/2807442.2807480
[11]
Yang Zhang, Gierad Laput, and Chris Harrison. 2017. Electrick: Low-Cost Touch Sensing Using Electric Field Tomography. In Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems (Denver, Colorado, USA) (CHI ’17). Association for Computing Machinery, New York, NY, USA, 1–14. https://doi.org/10.1145/3025453.3025842
[12]
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 (Tokyo, Japan) (UIST ’16). Association for Computing Machinery, New York, NY, USA, 843–850. https://doi.org/10.1145/2984511.2984574
[13]
Junyi Zhu, Jackson Snowden, Joshua Verdejo, Emily Chen, Paul Zhang, Hamid Ghaednia, Joseph H. Schwab, and Stefanie Mueller. 2021. EIT-kit: An Electrical Impedance Tomography Toolkit for Health and Motion Sensing. In Proceedings of the 34th Annual ACM Symposium on User Interface Software and Technology. https://doi.org/10.1145/3472749.3474758
[14]
Y Zou and Z Guo. 2003. A review of electrical impedance techniques for breast cancer detection. Medical Engineering & Physics 25, 2 (2003), 79 – 90. https://doi.org/10.1016/S1350-4533(02)00194-7
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October 2021
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Published: 10 October 2021
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- Demonstration
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UIST '21: The 34th Annual ACM Symposium on User Interface Software and Technology
October 10 - 14, 2021
Virtual Event, USA
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