On Spatial Diversity in WiFi-Based Human Activity Recognition: A Deep Learning-Based Approach

The deeply penetrated WiFi signals not only provide fundamental communications for the massive Internet of Things devices but also enable cognitive sensing ability in many other applications, such as human activity recognition. State-of-the-art WiFi-based device-free systems leverage the correlations between signal changes and body movements for human activity recognition. They have demonstrated reasonably good recognition results with a properly placed transceiver pair, or, in other words, when the human body is within a certain sweet zone. Unfortunately, the sweet zone is not ubiquitous. When the person moves out of the area and enters a dead zone, or even just the orientation changes, the recognition accuracy can quickly decay. In this paper, we closely examine such spatial diversity in WiFi-based human activity recognition. We identify the dead zones and their key influential factors, and accordingly present WiSDAR, a WiFi-based spatial diversity-aware device-free activity recognition system. WiSDAR overshadows the dead zones yet with only one physical WiFi sender and receiver. The key innovation is extending the multiple antennas of modern WiFi devices to construct multiple separated antenna pairs for activity observing. Profiling activity features from multiple spatial dimensions can be more complicated and offer much richer information for further recognition. To this end, we propose a deep learning-based framework that integrates the hidden features from both temporal and spatial dimensions, achieving highly accurate and reliable recognition results. WiSDAR is fully compatible with commercial off-the-shelf WiFi devices, and we have implemented it on the commonly available Intel WiFi 5300 cards. Our real-world experiments demonstrate that it recognizes human activities with a stable accuracy of around 96%.