Li, X, Chang, L, Song, F et al. (4 more authors) (2021) CrossGR: Accurate and Low-cost Cross-target Gesture Recognition using Wi-Fi. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 5 (1). 21. ISSN 2474-9567
Abstract
This paper focuses on a fundamental question in Wi-Fi-based gesture recognition: "Can we use the knowledge learned from some users to perform gesture recognition for others?". This problem is also known as cross-target recognition. It arises in many practical deployments of Wi-Fi-based gesture recognition where it is prohibitively expensive to collect training data from every single user. We present CrossGR, a low-cost cross-target gesture recognition system. As a departure from existing approaches, CrossGR does not require prior knowledge (such as who is currently performing a gesture) of the target user. Instead, CrossGR employs a deep neural network to extract user-agnostic but gesture-related Wi-Fi signal characteristics to perform gesture recognition. To provide sufficient training data to build an effective deep learning model, CrossGR employs a generative adversarial network to automatically generate many synthetic training data from a small set of real-world examples collected from a small number of users. Such a strategy allows CrossGR to minimize the user involvement and the associated cost in collecting training examples for building an accurate gesture recognition system. We evaluate CrossGR by applying it to perform gesture recognition across 10 users and 15 gestures. Experimental results show that CrossGR achieves an accuracy of over 82.6% (up to 99.75%). We demonstrate that CrossGR delivers comparable recognition accuracy, but uses an order of magnitude less training samples collected from the end-users when compared to state-of-the-art recognition systems.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2021 Association for Computing Machinery. This is an author produced version of an article published in Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies. Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | Cross-target, Wireless Sensing, Gesture Recognition, Wi-Fi, Deep 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: | 17 Feb 2021 13:45 |
Last Modified: | 16 Apr 2021 12:40 |
Status: | Published |
Publisher: | Association for Computing Machinery |
Identification Number: | 10.1145/3448100 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:170933 |