WiFi-enabled Device-free Gesture Recognition for Smart Home Automation

Abstract

Gesture recognition is playing a vital role in human-computer interaction and smart home automation. Conventional gesture recognition systems require either dedicated extra infrastructure to be deployed or users to carry wearable and mobile devices, which is high-cost and intrusive for large-scale implementation. In this paper, we propose FreeGesture, a device-free gesture recognition scheme that can automatically identify common gestures via deep learning using only commodity WiFi-enabled IoT devices. A novel OpenWrt-based IoT platform is developed so that the fine-grained Channel State Information (CSI) measurements can be obtained directly from IoT devices. Since these measurements are time-series data, we consider them as continuous RF images and construct CSI frames with both amplitudes and phase differences as features. We design a dedicated convolutional neural network (CNN) to uncover the discriminative local features in these CSI frames and construct a robust classifier for gesture recognition. All the parameters in CNN are automatically fine-tuned end-to-end. Experiments are conducted in a typical office and the results validate that FreeGesture achieves a 95.8% gesture recognition accuracy.