Air Gesture Recognition Using WLAN Physical Layer Information

Abstract

In recent years, the researchers have witnessed the important role of air gesture recognition in human-computer interactive (HCI), smart home, and virtual reality (VR). The traditional air gesture recognition method mainly depends on external equipment (such as special sensors and cameras) whose costs are high and also with a limited application scene. In this paper, we attempt to utilize channel state information (CSI) derived from a WLAN physical layer, a Wi-Fibased air gesture recognition system, namely, WiNum, which solves the problems of users’ privacy and energy consumption compared with the approaches using wearable sensors and depth cameras. In the process of recognizing the WiNum method, the collected raw data of CSI should be screened, among which can reflect the gesture motion. Meanwhile, the screened data should be preprocessed by noise reduction and linear transformation. After preprocessing, the joint of amplitude information and phase information is extracted, to match and recognize different air gestures by using the S-DTW algorithm which combines dynamic time warping algorithm (DTW) and support vector machine (SVM) properties. Comprehensive experiments demonstrate that under two different indoor scenes, WiNum can achieve higher recognition accuracy for air number gestures; the average recognition accuracy of each motion reached more than 93%, in order to achieve effective recognition of air gestures.

[Retracted] Research and Application of Multifeature Gesture Recognition in Human‐Computer Interaction Based on Virtual Reality Technology

“…The multifeature gesture changes a lot, and the final control efficiency will be significantly 3 Wireless Communications and Mobile Computing enhanced after the application of this tracking method. Using the HOG extraction method in multifeature gesture tracking, the gesture image will be subdivided into multiple units, and each unit has different features, so as to describe the edge image in the change of gesture [12]. And these subdivided cells are uniformly divided into the same area, so that the extracted gradient edge map is also connected together.…”

Wi-GC: A Deep Spatiotemporal Gesture Recognition Method Based on Wi-Fi Signal

“…It uses the K-means combined with the Bagging algorithm to optimize the SVM classification model and the average recognition rate reaches 95.8%. Dang et al [26] proposed a CSI-based aerial handwritten digit recognition system. First, the system selects the data that can reflect the gesture movement from the CSI raw data.…”

MoSeFi: Duration Estimation Robust Human Motion Sensing via Commodity WiFi Device

“…Compared with the RSSI, CSI describes the multipath propagation effects of wireless signals to a certain extent and provides more detailed and robust features for advanced environmental perception. Therefore, multiple CSI-based sensing methods have been proposed, including indoor location [4,5], intrusion detection [6,7], behavior classification [8,9], and gesture recognition [10][11][12].…”