Accurate detection of human presence in indoor environments is important for various applications, such as energy management and security. In this paper, 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) employs 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. Additionally, we utilize 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. Overall, our proposed ALPD system shows promising results for human presence detection using WiFi CSI signals.

翻译：准确检测室内环境中的人类存在对于能源管理和安全等多种应用至关重要。在本文中，我们提出了一种利用WiFi信号的信道状态信息（CSI）进行人类存在检测的新型系统。该系统名为注意力增强的深度学习存在检测（ALPD），采用注意力机制自动从CSI数据中选择信息子载波，并利用双向长短期记忆（LSTM）网络捕捉CSI中的时间依赖性。此外，我们利用静态特征来提高静态状态下人类存在检测的准确性。我们通过部署一对WiFi接入点（APs）收集CSI数据集来评估所提出的ALPD系统，并将其与多个基准方法进行比较。结果表明，我们的ALPD系统在准确性方面优于基准方法，尤其是在存在干扰的情况下。此外，双向传输数据有助于提高训练的稳定性和准确性，并降低训练数据收集的成本。总体而言，我们提出的ALPD系统在使用WiFi CSI信号进行人类存在检测方面展现了有前景的结果。