  The study of human gait recognition has been becoming an active research
field. In this paper, we propose to adopt the attention-based Recurrent Neural
Network (RNN) encoder-decoder framework to implement a cycle-independent human
gait and walking direction recognition system in Wi-Fi networks. For capturing
more human walking dynamics, two receivers together with one transmitter are
deployed in different spatial layouts. In the proposed system, the Channel
State Information (CSI) measurements from different receivers are first
gathered together and refined to form an integrated walking profile. Then, the
RNN encoder reads and encodes the walking profile into primary feature vectors.
Given a specific recognition task, the decoder computes a corresponding
attention vector which is a weighted sum of the primary features assigned with
different attentions, and is finally used to predict the target. The attention
scheme motivates our system to learn to adaptively align with different
critical clips of CSI data sequence for human walking gait and direction
recognitions. We implement our system on commodity Wi-Fi devices in indoor
environment, and the experimental results demonstrate that our system can
achieve average F1 scores of 89.69% for gait recognition from a group of 8
subjects and 95.06% for direction recognition from 8 walking directions, in
addition, the average accuracies of these two recognition tasks both exceed
97%.
