Journal
IEEE SENSORS JOURNAL
Volume 21, Issue 12, Pages 13522-13529Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JSEN.2021.3068388
Keywords
Human activity classification; FMCW radar; MIMO radar; point clouds; deep convolutional neural networks; deep recurrent neural networks
Funding
- Daegu Gyeongbuk Institute of Science and Technology (DGIST) Research and Development Program of the Ministry of Science and Information and Communications Technology [19-ST-01]
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The study investigates the feasibility of classifying human activities measured by MIMO radar in the form of a point cloud. By recognizing point cloud variations, human activities can be effectively classified. Using a DRNN and a convolutional neural network structure, accurate classification of human activities was achieved.
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, individual points form a point cloud whose shape resembles that of the human subject. As the subject engages in various activities, the shapes of the point clouds change accordingly. We propose to classify human activities through recognition of point cloud variations. To construct a dataset, we used an FMCW MIMO radar to measure 19 human subjects performing 7 activities. The radar had 12 TXs and 16 RXs, producing a 33 x 31 virtual array with approximately 3.5 degrees of angular resolution in azimuth and elevation. To classify human activities, we used a deep recurrent neural network (DRNN) with a two-dimensional convolutional network. The convolutional filters captured point clouds' features at time instance for sequential input into the DRNN, which recognized time-varying signatures, producing a classification accuracy exceeding 97%.
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