Attention-Based Gait Recognition and Walking Direction Estimation in Wi-Fi Networks


The majority of currently available gait recognition systems that rely on Wi-Fi consider gait cycle detection to be an essential process. However, because dynamic measurements obtained from commercial Wi-Fi devices contain a lot of background noise, it can be difficult to identify gait cycles. We adopt the attention-based Recurrent Neural Network (RNN) encoder-decoder and propose a cycle-independent human gait recognition and walking direction estimation system called AGait for use in Wi-Fi networks. AGait's full name is the Cycle-Independent Human Gait Recognition and Walking Direction Estimation System. Two receivers and one transmitter are placed in a variety of spatial configurations, and this is done so that more human walking dynamics can be captured. An integrated walking profile is created by first assembling the Channel State Information (CSI) collected from a variety of receivers and then refining it. The walking profile is then read by the RNN encoder, and the encoded data is converted into primary feature vectors. When the decoder is given a particular gait or direction sensing task, it will compute a particular attention vector that corresponds to the task, and this attention vector will ultimately be used to predict the target. The attention scheme is what motivates AGait to learn how to adaptively align with various critical clips of CSI data for the various tasks it needs to complete. The experimental results demonstrate that AGait can achieve average F1 scores of 97.32 to 89.77 percent for gait recognition from a group of 4 to 10 subjects and 97.41 percent for direction estimation from 8 walking directions when it is implemented on commercial Wi-Fi devices in three different indoor environments. This is demonstrated by the fact that AGait was implemented on commercial Wi-Fi devices.

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