Working Paper Article Version 1 This version is not peer-reviewed

Arm Motion Classification Using Time Series Analysis of the Spectrogram Frequency Envelopes

Version 1 : Received: 15 December 2019 / Approved: 16 December 2019 / Online: 16 December 2019 (11:42:44 CET)

A peer-reviewed article of this Preprint also exists.

Zeng, Z.; Amin, M.G.; Shan, T. Arm Motion Classification Using Time-Series Analysis of the Spectrogram Frequency Envelopes. Remote Sens. 2020, 12, 454. Zeng, Z.; Amin, M.G.; Shan, T. Arm Motion Classification Using Time-Series Analysis of the Spectrogram Frequency Envelopes. Remote Sens. 2020, 12, 454.

Journal reference: Remote Sens. 2020, 12, 454
DOI: 10.3390/rs12030454

Abstract

Hand and arm gesture recognition using radio frequency (RF) sensing modality proves valuable in man-machine interface and smart environment. In this paper, we use time series analysis method for accurately measuring 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 the dynamic time warping (DTW) method and compare its performance with that of the long short-term memory (LSTM) network. Both methods take into account the values as well as the temporal evolution and trends of time series data. It is shown that the DTW method achieves high gesture classification rates and is robust to time misalignment.

Subject Areas

arm motion recognition; micro-doppler signature; time series analysis; dynamic time warping; long short-term memory

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