Wu, Y;
Jiang, D;
Duan, J;
Liu, X;
Bayford, R;
Demosthenous, A;
(2018)
Towards a high accuracy wearable hand gesture recognition system using EIT.
In:
2018 IEEE International Symposium on Circuits and Systems (ISCAS).
IEEE: Florence, Italy, Italy.
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Abstract
This paper presents a high accuracy hand gesture recognition system based on electrical impedance tomography (EIT). The system interfaces the forearm using a wrist wrap with embedded electrodes. It measures the inner conductivity distributions caused by bone and muscle movement of the forearm in real-time and passes the data to a deep learning neural network for gesture recognition. The system has an EIT bandwidth of 500 kHz and a measured sensitivity in excess of 6.4 Ω per frame. Nineteen hand gestures are designed for recognition, and with the proposed round robin sub-grouping method, an accuracy of over 98% is achieved.
| Type: | Proceedings paper |
|---|---|
| Title: | Towards a high accuracy wearable hand gesture recognition system using EIT |
| Event: | 2018 IEEE International Symposium on Circuits and Systems (ISCAS) |
| ISBN-13: | 978-1-5386-4881-0 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1109/ISCAS.2018.8351296 |
| Publisher version: | http://dx.doi.org/10.1109/ISCAS.2018.8351296 |
| 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. |
| 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/10055502 |
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