  WiFi-based sensing for human activity recognition (HAR) has recently become a
hot topic as it brings great benefits when compared with video-based HAR, such
as eliminating the demands of line-of-sight (LOS) and preserving privacy.
Making the WiFi signals to 'see' the action, however, is quite coarse and thus
still in its infancy. An end-to-end spatiotemporal WiFi signal neural network
(STWNN) is proposed to enable WiFi-only sensing in both line-of-sight and
non-line-of-sight scenarios. Especially, the 3D convolution module is able to
explore the spatiotemporal continuity of WiFi signals, and the feature
self-attention module can explicitly maintain dominant features. In addition, a
novel 3D representation for WiFi signals is designed to preserve multi-scale
spatiotemporal information. Furthermore, a small wireless-vision dataset (WVAR)
is synchronously collected to extend the potential of STWNN to 'see' through
occlusions. Quantitative and qualitative results on WVAR and the other three
public benchmark datasets demonstrate the effectiveness of our approach on both
accuracy and shift consistency.
