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Accurate detection of human presence in indoor environments is important forvarious applications, such as energy management and security. In this paper, wepropose a novel system for human presence detection using the channel stateinformation (CSI) of WiFi signals. Our system named attention-enhanced deeplearning for presence detection (ALPD) employs an attention mechanism toautomatically select informative subcarriers from the CSI data and abidirectional long short-term memory (LSTM) network to capture temporaldependencies in CSI. Additionally, we utilize a static feature to improve theaccuracy of human presence detection in static states. We evaluate the proposedALPD system by deploying a pair of WiFi access points (APs) for collecting CSIdataset, which is further compared with several benchmarks. The resultsdemonstrate that our ALPD system outperforms the benchmarks in terms ofaccuracy, especially in the presence of interference. Moreover, bidirectionaltransmission data is beneficial to training improving stability and accuracy,as well as reducing the costs of data collection for training. Overall, ourproposed ALPD system shows promising results for human presence detection usingWiFi CSI signals.

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