Attention-Enhanced Deep Learning for Device-Free Through-the-Wall Presence Detection Using Indoor WiFi Systems
This work addresses a domain-specific problem for applications like energy management and security, representing an incremental improvement over existing methods.
The paper tackles human presence detection in indoor environments using WiFi channel state information, proposing an attention-enhanced deep learning system that outperforms benchmarks in accuracy, especially under interference.
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.