Abstract
Using deep-learning techniques for analyzing radar signatures has opened new possibilities in the field of smart-sensing, especially in the applications of hand-gesture recognition. In this paper, we present a framework, using deep-learning techniques, to classify hand-gesture signatures generated from an ultra-wideband (UWB) impulse radar. We extract the signals of 14 different hand-gestures and represent each signature as a 3-dimensional tensor consisting of range-Doppler frame sequence. These signatures are passed to a convolutional neural network (CNN) to extract the unique features of each gesture, and are then fed to a classifier. We compare 4 different classification architectures to predict the gesture class, namely; (i) fully connected neural network (FCNN), (ii) k-Nearest Neighbours (k-NN), (iii) support vector machine (SVM), (iv) long short term memory (LSTM) network. The shape of the range-Doppler-frame tensor and the parameters of the classifiers are optimized in order to maximize the classification accuracy. The classification results of the proposed architectures show a high level of accuracy above 96 % and a very low confusion probability even between similar gestures.
Highlights
H AND-GESTURE recognition is gaining significant research interest due to the wide range of envisioned applications
The common types of waveforms used in miniature radar sensors are; (i) continuous waveform (CW), (ii) pulses, and (iii) frequency modulated continuous waveform (FMCW)
We describe in Table. 1 some of the approaches found in the literature for hand-gesture recognition using radar sensors, including the utilized machine-learning algorithms, the type of radar waveform, and the set of gesture signatures used
Summary
H AND-GESTURE recognition is gaining significant research interest due to the wide range of envisioned applications. Despite the excellent ability to capture Doppler signatures, Doppler radars fail to extract the range information of the targets [9] due to the inherited narrow band nature of the waveform. The advantages of using UWB impulse radar for capturing the range-Doppler signatures compared to their counterparts are mainly; (i) low power consumption, (ii) fine range resolution, and (iii) the ability to detect very close targets [9]. These features make UWB impulse radar an excellent candidate for collecting hand-gestures. A framework for mapping the raw signal from a UWB impulse radar as a sequence of range-Doppler frames suitable for 3D deep-learning methods. Analytic formulation of key controllable parameters to optimize the classification performance
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