Attention-Enhanced Deep Learning for Device-Free Through-the-Wall Presence Detection Using Indoor WiFi Systems

Li Hsiang Shen, An Hung Hsiao, Kuan I. Lu, Kai Ten Feng*

*此作品的通信作者

研究成果: Article同行評審

3 引文 斯高帕斯(Scopus)

摘要

Accurate detection of human presence in indoor environments is important for various applications, such as energy management and security. In this article, we propose a novel system for human presence detection using the channel state information (CSI) of WiFi signals. Our system named attention-enhanced deep learning for presence detection (ALPD) uses an attention mechanism to automatically select informative subcarriers from the CSI data and a bidirectional long short-term memory (LSTM) network to capture temporal dependencies in CSI. In addition, we use a static feature to improve the accuracy of human presence detection in static states. We evaluate the proposed ALPD system by deploying a pair of WiFi access points (APs) for collecting CSI dataset, which is further compared with several benchmarks. The results demonstrate that our ALPD system outperforms the benchmarks in terms of accuracy, especially in the presence of interference. Moreover, bidirectional transmission data is beneficial to training improving stability and accuracy, as well as reducing the costs of data collection for training. To elaborate a little further, we have also evaluated the potential of ALPD for detecting more challenging human activities in multirooms. Overall, our proposed ALPD system shows promising results for human presence detection using WiFi CSI signals.

原文English
頁(從 - 到)5288-5302
頁數15
期刊IEEE Sensors Journal
24
發行號4
DOIs
出版狀態Published - 15 2月 2024

指紋

深入研究「Attention-Enhanced Deep Learning for Device-Free Through-the-Wall Presence Detection Using Indoor WiFi Systems」主題。共同形成了獨特的指紋。

引用此