Browse by author
Lookup NU author(s): Dr Bo WeiORCiD
Full text for this publication is not currently held within this repository. Alternative links are provided below where available.
© 2019 Association for Computing Machinery.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.
Author(s): Wei B, Hu W, Yang M, Chou CT
Publication type: Article
Publication status: Published
Journal: ACM Transactions on Sensor Networks
Year: 2019
Volume: 15
Issue: 3
Print publication date: 01/08/2019
Online publication date: 09/08/2019
Acceptance date: 01/05/2019
ISSN (print): 1550-4859
ISSN (electronic): 1550-4867
Publisher: Association for Computing Machinery
URL: https://doi.org/10.1145/3338026
DOI: 10.1145/3338026
Altmetrics provided by Altmetric
