Yao, Tianyang;
Wu, Yu;
Jiang, Dai;
Bayford, Richard;
Demosthenous, Andreas;
(2023)
A Compact Neural Network for High Accuracy Bioimpedance-Based Hand Gesture Recognition.
In:
2023 IEEE Biomedical Circuits and Systems Conference (BioCAS), Conference Proceedings.
(pp. pp. 1-5).
IEEE: Toronto, ON, Canada.
Preview |
Text
BIOCAS2023_finalDraft.pdf - Published Version Download (1MB) | Preview |
Abstract
This paper presents a compact customised neural network with 44 parameters for hand gesture recognition based on electrical impedance tomography (EIT) using a flexible 8-electrode band. The classification accuracy is improved by assigning higher weights to the impedances captured closer to the current injection position. The non-fully connected layer working as a spatial filter reduces the complexity of the network structure. Validated on a discrete EIT system, the proposed network structure can distinguish eight gestures with an accuracy of 99.49%. Towards a low-power wearable design, an analogue inference circuit based on the proposed network structure was also designed in 65 nm CMOS. The system features a low-power multi-output digital-to-analogue converter (DAC) to provide data for the analogue computation efficiently. This designed CMOS analogue inference has a recognition accuracy of 98.13%.
| Type: | Proceedings paper |
|---|---|
| Title: | A Compact Neural Network for High Accuracy Bioimpedance-Based Hand Gesture Recognition |
| Event: | 2023 IEEE Biomedical Circuits and Systems Conference (BioCAS) |
| Dates: | 19 Oct 2023 - 21 Oct 2023 |
| ISBN-13: | 979-8-3503-0026-0 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1109/BioCAS58349.2023.10388679. |
| Publisher version: | https://doi.org/10.1109/BioCAS58349.2023.10388679 |
| 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: | Training, Electrical impedance tomography, Power demand, Neural networks, Gesture recognition, Voltage, Spatial filters |
| 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-pp.ucl.ac.uk/id/eprint/10188439 |
Archive Staff Only
 |
View Item |
