# WiFi-Based Gesture Recognition for Vehicular Infotainment System--An Integrated Approach
## 2 Related Work  
In this section, we will briefly review the CSI-based activity and gesture recognition systems that exploit different classification methods including KNN and SRC algorithms. Fine-grained physical layer CSI holds pervasive indoor localization and has attracted many researchers because of its potential for accurate indoor localization. With the advancement in wireless technology and the ubiquitous deployment of indoor wireless systems, indoor localization has been permeated into a new era of modern life [27]. Recently, a WiFi-based training-free localization system has been presented with good performance [28].  
WiSee [29] is a WiFi based system that can recognize nine different gestures in line-of-sight (LOS), non line-of-sight (NLOS) and through-the-wall scenarios via Doppler shifts. Through-wall motion detection was investigated by WiVi [30] using multi antenna techniques. However, traditional solutions have been prototyped with special software or hardware to capture OFDM (Orthogonal Frequency Division Multiplexing) signals. As compared to these conventional methods, our proposed solution leverages off-the-shelf WiFi devices without any change in infrastructure.  
The emerging device-free localization and activity recognition relies on CSI for better characterization of WiFi signals influenced by the human activities [31; 32; 33; 34]. In recent years, CSI-based micro-activity recognition [35] and intrusion detection [36; 37; 38] systems have emerged with good recognition results.  
WiG [11] focused on WiFi-based gesture recognition for both LOS and NLOS scenarios. CSI-based gesture detection was presented in Reference [12] leveraging packet transmission and the recognition of gestures was performed by distinguishing their strengths. WiGer [39] demonstrated a WiFi-based gesture recognition system by designing a fast dynamic time warping algorithm to classify hand gestures. Recently, Reference [40] presented the writing in air with WiFi signals for virtual reality devices with more complexity and increased diversity as compared to simple gestures.  
Most of existing CSI-based systems rely on single classification algorithms. During the previous decade, KNN and SRC algorithms have been numerously used in various CSI-based device-free activity recognition models, as a stand-alone classifier. WiCatch [41] proposed WiFi-based hand gesture recognition system utilizing weak signals reflected from hands and the recognition is based on support vector machines (SVMs). Wi-Key [16] recognized keystrokes via CSI-waveform using the KNN classifier. WiFinger [13] leverages WiFi signals for finger gesture recognition by examining the unique patterns of CSI using the KNN classifier. The effect of Doppler shifts on CSI for health-care applications using the SRC classification algorithm was described in Reference [17]. Human activity recognition using the SRC classifier has been successfully demonstrated in Reference [18] with high accuracy for both LOS and NLOS scenarios leveraging CSI. Different from previous work, our classification algorithm is based on the integration of modified-KNN and SRC for automotive infotainment interface.  
A limited work is reported in the literature on WiFi-based driver's in-vehicle activity or gestures recognition. In recent years, WiFi-based driver fatigue detection and driver activity recognition systems have been investigated with some constraints. In this context, WiFind [42] is suitable only for fatigue detection, while WiDriver [43] leverages a driver's hand movements for better characterization of driver action recognition using WiFi signals. SafeDrive-Fi [44] demonstrated CSI-based dangerous driving recognition through body movements and gestures, using variance of CSI amplitude and phase measurements. Different from them, we have specifically focused on driver hand, finger and head gesture recognition for the application of in-vehicular infotainment system.