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Open AccessFeature PaperArticle

From Signal to Image: Enabling Fine-Grained Gesture Recognition with Commercial Wi-Fi Devices

National Defense Engineering College, Army Engineering University of PLA, Nanjing 210007, China
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Sensors 2018, 18(9), 3142; https://doi.org/10.3390/s18093142
Received: 11 August 2018 / Revised: 10 September 2018 / Accepted: 13 September 2018 / Published: 18 September 2018
(This article belongs to the Special Issue Mobile Computing and Ubiquitous Networking)
Gesture recognition acts as a key enabler for user-friendly human-computer interfaces (HCI). To bridge the human-computer barrier, numerous efforts have been devoted to designing accurate fine-grained gesture recognition systems. Recent advances in wireless sensing hold promise for a ubiquitous, non-invasive and low-cost system with existing Wi-Fi infrastructures. In this paper, we propose DeepNum, which enables fine-grained finger gesture recognition with only a pair of commercial Wi-Fi devices. The key insight of DeepNum is to incorporate the quintessence of deep learning-based image processing so as to better depict the influence induced by subtle finger movements. In particular, we make multiple efforts to transfer sensitive Channel State Information (CSI) into depth radio images, including antenna selection, gesture segmentation and image construction, followed by noisy image purification using high-dimensional relations. To fulfill the restrictive size requirements of deep learning model, we propose a novel region-selection method to constrain the image size and select qualified regions with dominant color and texture features. Finally, a 7-layer Convolutional Neural Network (CNN) and SoftMax function are adopted to achieve automatic feature extraction and accurate gesture classification. Experimental results demonstrate the excellent performance of DeepNum, which recognizes 10 finger gestures with overall accuracy of 98% in three typical indoor scenarios. View Full-Text
Keywords: gesture recognition; channel state information; image processing; deep learning gesture recognition; channel state information; image processing; deep learning
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Zhou, Q.; Xing, J.; Chen, W.; Zhang, X.; Yang, Q. From Signal to Image: Enabling Fine-Grained Gesture Recognition with Commercial Wi-Fi Devices. Sensors 2018, 18, 3142.

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