  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. To elaborate a
little further, we have also evaluated the potential of ALPD for detecting more
challenging human activities in multi-rooms. Overall, our proposed ALPD system
shows promising results for human presence detection using WiFi CSI signals.
