From Real to Complex: Enhancing Radio-based Activity Recognition Using Complex-Valued CSI

Wei, Bo, Hu, Wen, Yang, Mingrui and Chou, Chun Tung (2019) From Real to Complex: Enhancing Radio-based Activity Recognition Using Complex-Valued CSI. ACM Transactions on Sensor Networks, 15 (3). p. 35. ISSN 1550-4859

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Official URL: https://doi.org/10.1145/3338026

Abstract

Activity recognition is an important component of many pervasive computing applications. Radio-based activity recognition has the advantage that it does not have the privacy concern compared with camera-based solutions, and subjects do not have to carry a device on them. It has been shown channel state information (CSI) can be used for activity recognition in a device-free setting. With the proliferation of wireless devices, it is important to understand how radio frequency interference (RFI) can impact on pervasive computing applications. In this article, we investigate the impact of RFI on device-free CSI-based location-oriented activity recognition. We present data to show that RFI can have a significant impact on the CSI vectors. In the absence of RFI, different activities give rise to different CSI vectors that can be differentiated visually. However, in the presence of RFI, the CSI vectors become much noisier, and activity recognition also becomes harder. Our extensive experiments show that the performance may degrade significantly with RFI. We then propose a number of countermeasures to mitigate the impact of RFI and improve the performance. We are also the first to use complex-valued CSI along with the state-of-the-art Sparse Representation Classification method to enhance the performance in the environment with RFI.

Item Type: Article
Subjects: G400 Computer Science
Department: Faculties > Engineering and Environment > Computer and Information Sciences
Depositing User: Becky Skoyles
Date Deposited: 31 May 2019 07:17
Last Modified: 11 Oct 2019 10:27
URI: http://nrl.northumbria.ac.uk/id/eprint/39427

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