Jiang, D;
Wu, Y;
Demosthenous, A;
(2020)
Hand Gesture Recognition Using Three-Dimensional Electrical Impedance Tomography.
IEEE Transactions on Circuits and Systems II: Express Briefs
, 67
(9)
pp. 1554-1558.
10.1109/TCSII.2020.3006430.
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Abstract
This brief presents a 16-electrode electrical impedance tomography (EIT) system for hand gesture recognition. The hardware of the system is based on integrated circuits including a 12-bit high spectral purity current-steering DAC implemented in 0.18 μm CMOS technology, a current driver and an instrumentation amplifier in 0.35 μm CMOS technology. Both 2D and 3D EIT electrode arrangements were tested for hand gesture recognition. It is shown that using machine learning algorithms, eight hand gestures can be distinguished from the measured bio-impedance data with an accuracy of 97.9% when the electrodes are placed on a single wristband, and an accuracy of 99.5% with the same number of electrodes distributed on two wristbands for 3D EIT measurement. In particular 3D EIT demonstrated significant superiority in its ability to discriminate between gestures with similar muscle contractions.
Type: | Article |
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Title: | Hand Gesture Recognition Using Three-Dimensional Electrical Impedance Tomography |
Location: | ELECTR NETWORK |
Open access status: | An open access version is available from UCL Discovery |
DOI: | 10.1109/TCSII.2020.3006430 |
Publisher version: | https://doi.org/10.1109/TCSII.2020.3006430 |
Language: | English |
Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
Keywords: | 3D electrical impedance tomography (EIT), current-steering DAC, hand gesture recognition, machine learning |
UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Electronic and Electrical Eng |
URI: | https://discovery.ucl.ac.uk/id/eprint/10110624 |
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