In-Vehicle CSI Gesture Recognition Using Two Wi-Fi Receivers With FEDRT-LSTM Model

Abstract

Existing in-vehicle gesture recognition using channel state information (CSI) of Wi-Fi signals requires the use of 3 or more receivers. Due to the lack of research on gesture recognition using fewer receivers, we here study the case of only two receivers. CSI data of 4 gestures are collected by two in-vehicle receivers and are then processed to generate body velocity profile (BVP) data. We propose a Transformer and long short-term memory (LSTM) (Transformer-LSTM) fusion model with a feature extraction and dimensionality reduction module (FEDRT-LSTM) to recognize gestures using BVP data from two-receiver CSI. The performance is compared with those of deep learning neural network (DNN), gated recurrent unit (GRU) capsules network (GRU-CapNet), LeNet-5, and GRU network models with hyperparameters optimized. Results show that the FEDRT-LSTM model can achieve the best recognition accuracy of 95.54%. This approach can reduce the number of receivers to two and achieve good performance in the recognition of 4 gestures.

Keywords

• In-vehicle
• CSI
• Transformer
• LSTM
• hyperparameter optimization