# WiFi-Based Gesture Recognition for Vehicular Infotainment System--An Integrated Approach
## 1 Introduction
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Gesture Type** & **Gesture Performed** & **Associated Task** & **Gesture Label** \\ \hline \multirow{8}{*}{Hand Gesture} & Swipe left & (\(\ast\)) Change channel & SL \\ \cline{2-4}  & Swipe right & (\(-\)) Change channel & SR \\ \cline{2-4}  & Hand going up & (\(+\)) Volume & HU \\ \cline{2-4}  & Hand going down & (\(-\)) Volume & HD \\ \cline{2-4}  & Flick & Zoom in & FK \\ \cline{2-4}  & Grab & Zoom out & GB \\ \cline{2-4}  & Push hand forward & (\(+\)) Temperature setting & PF \\ \cline{2-4}  & Pull hand backward & (\(-\)) Temperature setting & PB \\ \cline{2-4}  & Rotate hand clock-wise & (\(+\)) Fan speed & RC \\ \cline{2-4}  & Rotate hand anti-clock-wise & (\(-\)) Fan speed & RA \\ \hline \multirow{8}{*}{Finger Gesture} & Swipe V & Open function & SV \\ \cline{2-4}  & Swipe X & Close function & SX \\ \cline{1-1} \cline{2-4}  & Swipe + & Play next track & SP \\ \cline{1-1} \cline{2-4}  & Swipe \(-\) & Play previous track & SN \\ \cline{1-1} \cline{2-4}  & Head Gesture & Head tilting down & Pick phone call & HM \\ \cline{1-1} \cline{2-4}  & Head tilting right & Do not pick call & HR \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Proposed gestures utilized for different secondary tasks. In our proposed scheme, the superiority of SRC is applied on a nearest neighbor rule, to further improve the performance of SRC with reduced computational cost. Therefore, the advanced classification is based on the integrated properties of SRC and modified-KNN to produce efficient results. We utilize the Mean of Nearest Neighbors (MNN); a variant of KNN. The concept of the mean-based algorithm was originally proposed by [PERSON] and [PERSON] [19] to classify query patterns, using a local mean-based non-parametric classifier. The idea of local mean-based KNN has been successfully applied in various pattern recognition systems [20; 21]. Our prototype is based on multiple variants of KNN and SRC algorithms in the literature [22; 23; 24; 25; 26]. Different from others, we incorporate MNN with SRC for significant improvement in device-free gesture recognition for the application of automotive infotainment system, exploiting CSI. The combination of MNN and SRC has a computational advantage over using only SRC. We integrate MNN and SRC to overcome the computational complexity of SRC. Our proposed system leverages off-the-shelf WiFi devices to collect channel information, which are readily available in the form of CSI measurements on commercial WiFi devices. Our algorithm utilizes the change in WiFi channel information caused by driver gestures. The driver needs to perform specific gestures in the WiFi coverage area. As far as we know, this is the first attempt towards CSI-based device-free driver gesture recognition using nearest neighbor induced sparse representation for the application of automotive interface. Our contributions can be summarized as follows:  
* We present a WiFi-based device-free innovative framework to address the problem of driver gesture recognition for the application of vehicle infotainment systems leveraging CSI measurements. * We demonstrate a novel classification model by integrating SRC and a variant of the KNN algorithm to overcome the problem of expensive computational cost. * To evaluate the performance of our proposed framework, we perform comprehensive experiments in promising application scenarios. * To validate the results, we compare our system performance with state-of-the-art methods. The rest of the paper is organized as follows; Section 2, demonstrates the traditional techniques that are relevant to our work. In Section 3, we provide an overview of our proposed system. Section 4 presents the detailed system flow with the methodology of our proposed solution. Section 5 highlights the experimental settings and validates our results by way of performance evaluation. In Section 6, we discuss important aspects and limitations of the proposed framework. Finally, we conclude our work with some future suggestions in Section 7.