WiFi CSI Based Passive Human Activity Recognition Using Attention Based BLSTM

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WiFi CSI Based Passive Human Activity Recognition Using Attention Based BLSTM
Title:
WiFi CSI Based Passive Human Activity Recognition Using Attention Based BLSTM
Other Titles:
IEEE Transactions on Mobile Computing
Publication Date:
30 October 2018
Citation:
Chen, Z., Zhang, L., Jiang, C., Cao, Z., & Cui, W. (2019). WiFi CSI Based Passive Human Activity Recognition Using Attention Based BLSTM. IEEE Transactions on Mobile Computing, 18(11), 2714–2724. doi:10.1109/tmc.2018.2878233
Abstract:
Human activity recognition can benefit various applications including healthcare services and context awareness. Since human actions will influence WiFi signals, which can be captured by the channel state information (CSI) of WiFi, WiFi CSI based human activity recognition has gained more and more attention. Due to the complex relationship between human activities and WiFi CSI measurements, the accuracies of current recognition systems are far from satisfactory. In this paper, we propose a new deep learning based approach, i.e., attention based bi-directional long short-term memory (ABLSTM), for passive human activity recognition using WiFi CSI signals. The BLSTM is employed to learn representative features in two directions from raw sequential CSI measurements. Since the learned features may have different contributions for final activity recognition, we leverage on an attention mechanism to assign different weights for all the learned features. Real experiments have been carried out to evaluate the performance of the proposed ABLSTM for human activity recognition. The experimental results show that our proposed ABLSTM is able to achieve the best recognition performance for all activities when compared with some benchmark approaches.
License type:
Publisher Copyright
Funding Info:
This research / project is supported by the Agency for Science, Technology and Research - RIE2020 IAF-PP Grant - Industrial Internet of Things Research Program
Grant Reference no. : A1788a0023

This work is supported by Shandong Province Natural Science Foundation.
Description:
© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
ISSN:
1536-1233
1558-0660
2161-9875
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