Device-Free WiFi Human Sensing: From Pattern-Based to Model-Based Approaches

Recently, device-free WiFi CSI-based human behavior recognition has attracted a great amount of interest as it promises to provide a ubiquitous sensing solution by using the pervasive WiFi infrastructure. While most existing solutions are pattern-based, applying machine learning techniques, there is a recent trend of developing accurate models to reveal the underlining radio propagation properties and exploit models for fine-grained human behavior recognition. In this article, we first classify the existing work into two categories: Pattern-based and model-based recognition solutions. Then we review and examine the two approaches together with their enabled applications. Finally, we show the favorable properties of model-based approaches by comparing them using human respiration detection as a case study, and argue that our proposed Fresnel zone model could be a generic one with great potential for device-free human sensing using fine-grained WiFi CSI.