Human Activity Classification Based on Point Clouds Measured by Millimeter Wave MIMO Radar With Deep Recurrent Neural Networks

• Published in: IEEE sensors journal Vol. 21; no. 12; pp. 13522 - 13529
• Main Authors: Kim, Youngwook, Alnujaim, Ibrahim, Oh, Daegun
• Format: Journal Article
• Language: English
• Published: New York IEEE 15.06.2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Angular resolution; Azimuth; Classification; deep convolutional neural networks; deep recurrent neural networks; Feature extraction; FMCW radar; Human activity classification; Human performance; Human subjects; Millimeter wave radar; Millimeter waves; MIMO radar; Neural networks; point clouds; Radar; Radar antennas; Radar equipment; Radar measurements; Recurrent neural networks; Shape; Three-dimensional displays

We investigate the feasibility of classifying human activities measured by a MIMO radar in the form of a point cloud. If a human subject is measured by a radar system that has a very high angular azimuth and elevation resolution, scatterers from the body can be localized. When precisely represented,...