Abstract
Recently, researchers around the world have been striving to develop and modernize human–computer interaction systems by exploiting advances in modern communication systems. The priority in this field involves exploiting radio signals so human–computer interaction will require neither special devices nor vision-based technology. In this context, hand gesture recognition is one of the most important issues in human–computer interfaces. In this paper, we present a novel device-free WiFi-based gesture recognition system (WiGeR) by leveraging the fluctuations in the channel state information (CSI) of WiFi signals caused by hand motions. We extract CSI from any common WiFi router and then filter out the noise to obtain the CSI fluctuation trends generated by hand motions. We design a novel and agile segmentation and windowing algorithm based on wavelet analysis and short-time energy to reveal the specific pattern associated with each hand gesture and detect duration of the hand motion. Furthermore, we design a fast dynamic time warping algorithm to classify our system’s proposed hand gestures. We implement and test our system through experiments involving various scenarios. The results show that WiGeR can classify gestures with high accuracy, even in scenarios where the signal passes through multiple walls.
Highlights
Gesture recognition systems have become increasingly interesting to researchers in the field of human–computer interfaces
We present a gesture recognition system that enables humans to interact with WiFi-connected devices throughout the entire home, using seven hand gestures as interactive gestures, three air-drawn hand gestures that function in the security scheme as users’ authenticated gestures, and three additional air-drawn hand gestures that function as device selection gestures
For a detection point (DP), we use a Lenovo x201 laptop equipped with an IWL 5300 network card, running a 32-bit Linux operating system (Ubuntu version 14.04), and with the open source Channel State Information (CSI)-TOOLS installed [36]
Summary
Gesture recognition systems have become increasingly interesting to researchers in the field of human–computer interfaces. Building interactive systems based on wireless signals (such as ubiquitous WiFi) that do not require installing cameras or sensors will permanently change the computing industry and smart device manufacturing: for example, when manufacturing a smart interactive TV, manufacturers would not need to equip the TV with expensive sensors or vision-based technologies; instead, they could adopt device-free gesture recognition technology. This technology has the potential to provide a tremendous advancement in the field of human–computer interaction that will affect both smart home systems and smart device manufacturing. Previous approaches show that WiFi signal analysis can support localizing humans in indoor environments both when in line-of-sight (LOS) and non-line-of-sight (NLOS), such as through walls and behind closed doors
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