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Open AccessArticle

WiFi-Based Gesture Recognition for Vehicular Infotainment System—An Integrated Approach

Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian 116023, China
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Appl. Sci. 2019, 9(24), 5268; https://doi.org/10.3390/app9245268
Received: 10 November 2019 / Revised: 26 November 2019 / Accepted: 29 November 2019 / Published: 4 December 2019
(This article belongs to the Special Issue Indoor Localization Systems: Latest Advances and Prospects)
In the realm of intelligent vehicles, gestures can be characterized for promoting automotive interfaces to control in-vehicle functions without diverting the driver’s visual attention from the road. Driver gesture recognition has gained more attention in advanced vehicular technology because of its substantial safety benefits. This research work demonstrates a novel WiFi-based device-free approach for driver gestures recognition for automotive interface to control secondary systems in a vehicle. Our proposed wireless model can recognize human gestures very accurately for the application of in-vehicle infotainment systems, leveraging Channel State Information (CSI). This computationally efficient framework is based on the properties of K Nearest Neighbors (KNN), induced in sparse representation coefficients for significant improvement in gestures classification. In this typical approach, we explore the mean of nearest neighbors to address the problem of computational complexity of Sparse Representation based Classification (SRC). The presented scheme leads to designing an efficient integrated classification model with reduced execution time. Both KNN and SRC algorithms are complimentary candidates for integration in the sense that KNN is simple yet optimized, whereas SRC is computationally complex but efficient. More specifically, we are exploiting the mean-based nearest neighbor rule to further improve the efficiency of SRC. The ultimate goal of this framework is to propose a better feature extraction and classification model as compared to the traditional algorithms that have already been used for WiFi-based device-free gesture recognition. Our proposed method improves the gesture recognition significantly for diverse scale of applications with an average accuracy of 91.4%. View Full-Text
Keywords: driver distraction; human gesture recognition; device-free localization; wireless sensor; Channel State Information; integrated classification; computational complexity; sparse representation driver distraction; human gesture recognition; device-free localization; wireless sensor; Channel State Information; integrated classification; computational complexity; sparse representation
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Akhtar, Z.U.A.; Wang, H. WiFi-Based Gesture Recognition for Vehicular Infotainment System—An Integrated Approach. Appl. Sci. 2019, 9, 5268.

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