Zhai, S, Ye, G, Tang, Z et al. (4 more authors) (2021) Towards practical 3D ultrasound sensing on commercial-off-the-shelf mobile devices. Computer Networks, 191. 107990. ISSN 1389-1286
Abstract
Ultrasound based contactless sensing has the potential to extend the interactive range of mobile devices significantly. However, existing approaches either require multiple transceiver pairs that are not available on commercial mobile devices or can only recognize simple actions with one single stroke. These drawbacks significantly limit the practicability of prior work. This article presents UltraScr, an ultrasound sensing system that can recognize the sophisticated gestures with multiple strokes (e.g., writing a capital letter) using just a single sound transceiver pair. To do so, UltraScr first uses the frequency attenuation profile (FAP) to capture the subtle hand movements. It then employs a convolutional neural network (CNN) to extract the subtle hand movements’ discriminative features to build an accurate ultrasound sensing system. We go further by exploiting the rejection classification method (RCM) and incremental learning to improve the robustness of our sensing system in the end-user environment. We evaluate UltraScr by applying it to gesture recognition across different scenarios and users. Extensive experimental results show that while using only one transceiver pair, the performance of UltraScr is comparable to the multi-transceiver-paired implementations. We show that UltraScr is robust to the change of external conditions (i.e., different humidity and battery) and can work effectively on a wide range of locations, but requires 3x fewer training samples compared to the state-of-the-art.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2021, published by Elsevier B.V. All rights reserved. This is an author produced version of an article published in Computer Networks. Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | Wireless sensing; Frequency attenuation profile; Ultrasound sensing; Machine learning |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Computing (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 15 Mar 2021 16:42 |
Last Modified: | 11 Mar 2022 01:38 |
Status: | Published |
Publisher: | Elsevier |
Identification Number: | 10.1016/j.comnet.2021.107990 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:172138 |