# WiFi-Based Gesture Recognition for Vehicular Infotainment System--An Integrated Approach
## 1 Introduction  
Distracted driving is one of the main concerns that compromise road safety. A large number of road accidents are reported because of driver's engagement in performing conventional secondary tasks using visual-manual interfaces. With the advancements in vehicular technology and the introduction of human computer interaction (HCI), gesture-based touchless automotive interfaces are being incorporated in vehicle designs to reduce driver visual distraction. Therefore, driver gesture recognition has become the most interesting research topic in recent years. Human gestures recognition has been widely explored in the literature for a variety of applications to reduce the complexity of human interaction with computers and other digital interfaces [1; 2; 3; 4]. Existing research on in-vehicle gesture recognition is mainly focused on sensors, radars, or cameras [5; 6; 7], which have their own limitations in practical scenarios [8; 9]. Subjectively, haptified gestures with ultrasound are being incorporated in the automotive domain [10]. During the last decades, WiFi-based human gesture recognition systems have gained more attention, exploitingChannel State Information (CSI). Because of its simplicity and easy deployment, these systems are numerously used in various human-centric applications including emergency surveillance, health monitoring and assisted living [11; 12; 13; 14; 15]. WiFi-based device-free approach opened a new window for the researchers to further integrate wireless human gestures recognition in vehicular technology. We are motivated to investigate WiFi-based in-vehicle human gestures to control automotive secondary functions, which is device-free and non-intrusive to driver. This research is relevant to the ability of an intelligent vehicle to better characterize driver gestures for automotive infotainment (information and entertainment) system such as adjusting radio volume, fan speed and so on. We utilize device-free human-gestures based on the motion of the driver's hand, finger and head, as shown in Table 1. In this novel research, we particularly identify sixteen common gestures, of which ten are hand gestures (swipe left, swipe right, hand going up, hand going down, flick, grab, push hand forward, pull hand backward, rotate hand clock-wise, rotate hand anti-clock-wise), while four are finger gestures (swipe V, swipe X, swipe +, swipe -) and the remaining two are head gestures (head tilting down, head tilting right). The key challenge is how to extend the robust gesture recognition rate for the application of an in-vehicle infotainment system using WiFi signals. In order to address the problem, we are inspired to fill in this research gap using an integrated classification approach leveraging CSI measurements. In this paper, we present a device-free wireless innovative framework to address the problem of driver gesture recognition by integrating Sparse Representation based Classification (SRC) and a modified variant of the K Nearest Neighbors (KNN) algorithm for classification. To achieve this goal, we have to face several challenges because an uncontrolled in-vehicle environment is quite different from indoor environments. We perform extensive experiments and design our prototype to combat these challenges. The classification algorithm plays a vital role in the gesture recognition framework. Most of the existing CSI-based gesture recognition systems rely on a single classification algorithm, which cannot be guaranteed for maximum performance in vehicular technology. Carefully integrating two or more classification algorithms may enhance the recognition performance of a classifier for automotive interfaces. In this context, both KNN and SRC classifiers have been efficiently used as stand-alone, to solve various classification problems in WiFi-based device-free localization and activity or gesture recognition [16; 17; 18; 13].