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Real-time capable micro-Doppler signature decomposition of walking human limbs

: Abdulatif, S.; Aziz, F.; Kleiner, B.; Schneider, U.


Sego, D.J. ; Institute of Electrical and Electronics Engineers -IEEE-:
IEEE Radar Conference 2017 : 8-12 May, 2017, The Westin Seattle, Seattle, WA, USA
Piscataway, NJ: IEEE, 2017
ISBN: 978-1-4673-8823-8
ISBN: 978-1-4673-8824-5
Radar Conference (RadarConf) <2017, Seattle/Wash.>
Fraunhofer IPA ()

Unique micro-Doppler signature (μ-D) of a human body motion can be analyzed as the superposition of different body parts μ-D signatures. Extraction of human limbs μ-D signatures in real-time can be used to detect, classify and track human motion especially for safety application. In this paper, two methods are combined to simulate μ-D signatures of a walking human. Furthermore, a novel limbs μ-D signature time independent decomposition feasibility study is presented based on features as μ-D signatures and range profiles also known as micro-Range (μ-R). Walking human body parts can be divided into four classes (base, arms, legs, feet) and a decision tree classifier is used. Validation is done and the classifier is able to decompose μ-D signatures of limbs from a walking human signature on real-time basis.