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*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

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.

Original languageEnglish
Pages (from-to)5288-5302
Number of pages15
JournalIEEE Sensors Journal
Volume24
Issue number4
DOIs
StatePublished - 15 Feb 2024

Keywords

  • Autoencoder
  • channel state information (CSI)
  • deep learning
  • human presence detection
  • wireless sensing

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