  Human Activity Recognition (HAR) has recently received remarkable attention
in numerous applications such as assisted living and remote monitoring.
Existing solutions based on sensors and vision technologies have obtained
achievements but still suffering from considerable limitations in the
environmental requirement. Wireless signals like WiFi-based sensing have
emerged as a new paradigm since it is convenient and not restricted in the
environment. In this paper, a new WiFi-based and video-based neural network
(WiNN) is proposed to improve the robustness of activity recognition where the
synchronized video serves as the supplement for the wireless data. Moreover, a
wireless-vision benchmark (WiVi) is collected for 9 class actions recognition
in three different visual conditions, including the scenes without occlusion,
with partial occlusion, and with full occlusion. Both machine learning methods
- support vector machine (SVM) as well as deep learning methods are used for
the accuracy verification of the data set. Our results show that WiVi data set
satisfies the primary demand and all three branches in the proposed pipeline
keep more than $80\%$ of activity recognition accuracy over multiple action
segmentation from 1s to 3s. In particular, WiNN is the most robust method in
terms of all the actions on three action segmentation compared to the others.
