Abstract
Hand and arm gesture recognition using radio frequency (RF) sensing modality proves valuable in man–machine interfaces and smart environments. In this paper, we use the time-series analysis method to accurately measure the similarity of the micro-Doppler (MD) signatures between the training and test data, thus providing improved gesture classification. We characterize the MD signatures by the maximum instantaneous Doppler frequencies depicted in the spectrograms. In particular, we apply two machine learning (ML) techniques, namely, the dynamic time warping (DTW) method and the long short-term memory (LSTM) network. Both methods take into account the values as well as the temporal evolution and characteristics of the time-series data. It is shown that the DTW method achieves high gesture classification rates and is robust to time misalignment.
Highlights
Propelled by successes in discriminating between different human activities, radar has recently been employed for automatic hand gesture recognition for interactive intelligent devices [1,2,3,4,5,6]
The classification accuracy is used to evaluate the performance of the two machine learning (ML) methods, and all the classification results are obtained through 500 Monte Carlo trials
We considered a time-series analysis method for effective automatic arm motion recognition based on radar MD signature envelopes
Summary
Propelled by successes in discriminating between different human activities, radar has recently been employed for automatic hand gesture recognition for interactive intelligent devices [1,2,3,4,5,6]. This recognition proves important in contactless close-range hand-held or arm-worn devices, such as cell phones and watches. The most recent project on hand gesture recognition, Soli, by Google, monitors contactless interactions with radar embedded in a wrist band and is a good example of this emerging technology [3].
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