Using Electrical Impedance Tomography to Control a Robot

Authors: Shayan Rezvanigilkolaei, Shayesteh Vefaghnematollahi

Abstract:

Electrical impedance tomography is a non-invasive medical imaging technique suitable for medical applications. This paper describes an electrical impedance tomography device with the ability to navigate a robotic arm to manipulate a target object. The design of the device includes various hardware and software sections to perform medical imaging and control the robotic arm. In its hardware section an image is formed by 16 electrodes which are located around a container. This image is used to navigate a 3DOF robotic arm to reach the exact location of the target object. The data set to form the impedance imaging is obtained by having repeated current injections and voltage measurements between all electrode pairs. After performing the necessary calculations to obtain the impedance, information is transmitted to the computer. This data is fed and then executed in MATLAB which is interfaced with EIDORS (Electrical Impedance Tomography Reconstruction Software) to reconstruct the image based on the acquired data. In the next step, the coordinates of the center of the target object are calculated by image processing toolbox of MATLAB (IPT). Finally, these coordinates are used to calculate the angles of each joint of the robotic arm. The robotic arm moves to the desired tissue with the user command.

Keywords: Electrical impedance tomography, EIT, Surgeon robot, image processing of Electrical impedance tomography.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1111933

References:

[1] L.Charles, T, “The neuroscience of human movement”, ISBN: 964-452-209-5.
[2] T. Dudykevych .H, Richter, “Software and Operational Concept for EIT-Based regional lung Function monitoring”, Institute of Computer Science, Clausthal University of Technology, ISSN: 1860-8477, 2005.
[3] V. A. Cherepenin, A. Y. Karpov, A. V. Korjenevsky, Member IEEE,” Three-Dimensional EIT Imaging of Breast Tissues System Design and Clinical Testing,” IEEE Transactions on Medical Imaging, Vol. 21, no. 6, June 2002.
[4] A. Romsauerova, A. McEwan, L Horesh1, R. Yerworth, R. H. Bayford,” Multi-frequency electrical impedance tomography (EIT) of the adult human head: initial findings in brain tumors, arteriovenous malformations and chronic stroke, development of an analysis method and calibration,” Institute of Physics Publishing Physiological Measurement Physiol. Meas. 27. S147–S161, 2006.
[5] 1 MSPS, 12-Bit Impedance Converter, Network Analyzer, AD5933, Datasheet, Rev.E, http://www.analog.com/media/en/technical-documentation/data-sheets/AD5933.pdf.
[6] LPC1769/68/67/66/65/64/63, Product data sheet, Rev. 9.6 — 18 August 2015, http://www.nxp.com/documents/data_sheet/LPC1769_68_67_66_65_64_63.pdf.
[7] High accuracy impedance measurements using 12-Bit impedance Converters, AD5933, Circuit note, CN-0217, Rev.A, http://www.analog.com/media/en/reference-design-documentation/reference-designs/CN0217.pdf
[8] Gonzalez, R. C. Woods, R. H. and Eddins, S. L. "Digital image processing using MATLAB”, 2nd ed. (2004), Pearson Prentice Hall, pp. 347, 404.
[9] Image Processing Toolbox, Morphology Fundamentals: Dilation and Erosion, Mathworks, R2015b, http://www.mathworks.com/help/images/ morphology-fundamentals-dilation-and-erosion.html
[10] Otsu, N., "A Threshold Selection Method from Gray-Level Histograms," IEEE Transactions on Systems, Man, and Cybernetics, Vol. 9, No. 1, 1979, pp. 62-66.
[11] D. S. Holder,” Electrical Impedance Tomography of brain function,” Departments of Medical Physics, University College London and Clinical Neurophysiology, University College Hospital, London, UK,2009.