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WISDOM: Wi-Fi-Based Contactless Multiuser Activity Recognition
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AI
DOI：10.1109/JIOT.2022.3210131.png)
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摘要
En 中文
Wi-Fi-based contactless activity recognition is of great importance to computer-human interaction, accounting for convenience concerns. However, it remains challenging to recognize activities from multiple users due to the multipath distortion and disruption of Wi-Fi signals. In this article, we propose a highly universal framework, namely, WISDOM, for Wi-Fi-based multiuser activity recognition. Specifically, we first leverage an existing model to identify the number of users from the input Wi-Fi signals. Then, we develop a subcarrier correlation and inversion-based sorting algorithm to extract the signal for each user. Finally, we design a neural network, i.e., WISDOM-Net, which is built on a bidirectional gated recurrent unit network incorporated with the attention mechanism and the one dimension convolutional neural network, to recognize the corresponding user activities. Experimental results show that our proposed WISDOM-Net outperforms the existing baselines on both the public and our own data sets. In particular, WISDOM-Net can reach an average recognition accuracy of up to 98.19% and 90.77% in 2-user and 3-user scenarios, respectively.
Keyword:
Activity recognition
Wireless fidelity
Feature extraction
Internet of Things
Transmitters
Receivers
Monitoring
Blind signal separation
channel state information (CSI)
multiuser activity recognition
object sensing
Wi-Fi
期刊
IF:
8.9
论文数:
1.4W
被引数:
7.8W
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