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From real to complex: Enhancing radio-based activity recognition using complex-valued CSI

Lookup NU author(s): Dr Bo WeiORCiD


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© 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.

Publication metadata

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


DOI: 10.1145/3338026


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