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Sensors 2016, 16(6), 885; doi:10.3390/s16060885

Detection and Classification of Finer-Grained Human Activities Based on Stepped-Frequency Continuous-Wave Through-Wall Radar

Department of Medical Electronics, School of Biomedical Engineering, Fourth Military Medical University, Xi’an 710032, China
The first two authors Fugui Qi and Fulai Liang contributed equally to this work and should be regarded as co-first authors.
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Academic Editor: Vittorio M. N. Passaro
Received: 30 March 2016 / Revised: 10 June 2016 / Accepted: 10 June 2016 / Published: 15 June 2016
(This article belongs to the Section Physical Sensors)
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Abstract

The through-wall detection and classification of human activities are critical for anti-terrorism, security, and disaster rescue operations. An effective through-wall detection and classification technology is proposed for finer-grained human activities such as piaffe, picking up an object, waving, jumping, standing with random micro-shakes, and breathing while sitting. A stepped-frequency continuous wave (SFCW) bio-radar sensor is first used to conduct through-wall detection of finer-grained human activities; Then, a comprehensive range accumulation time-frequency transform (CRATFR) based on inverse weight coefficients is proposed, which aims to strengthen the micro-Doppler features of finer activity signals. Finally, in combination with the effective eigenvalues extracted from the CRATFR spectrum, an optimal self-adaption support vector machine (OS-SVM) based on prior human position information is introduced to classify different finer-grained activities. At a fixed position (3 m) behind a wall, the classification accuracies of six activities performed by eight individuals were 98.78% and 93.23%, respectively, for the two scenarios defined in this paper. In the position-changing experiment, an average classification accuracy of 86.67% was obtained for five finer-grained activities (excluding breathing) of eight individuals within 6 m behind the wall for the most practical scenario, a significant improvement over the 79% accuracy of the current method. View Full-Text
Keywords: finer-grained human activity; comprehensive range accumulation; human micro-Doppler; through-wall; support vector machine finer-grained human activity; comprehensive range accumulation; human micro-Doppler; through-wall; support vector machine
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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. (CC BY 4.0).

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MDPI and ACS Style

Qi, F.; Liang, F.; Lv, H.; Li, C.; Chen, F.; Wang, J. Detection and Classification of Finer-Grained Human Activities Based on Stepped-Frequency Continuous-Wave Through-Wall Radar. Sensors 2016, 16, 885.

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