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.
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.
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.
Keywords
arm motion recognition; micro-doppler signature; time series analysis; dynamic time warping; long short-term memory
Subject
Engineering, Electrical and Electronic Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
