Human Activity Classification Based on Point Clouds Measured by Millimeter Wave MIMO Radar With Deep Recurrent Neural Networks
| Autor: | Ibrahim Alnujaim, Daegun Oh, Youngwook Kim |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Computer science
business.industry 010401 analytical chemistry Feature extraction Point cloud Elevation Pattern recognition 01 natural sciences 0104 chemical sciences law.invention Azimuth Recurrent neural network law Extremely high frequency Angular resolution Artificial intelligence Electrical and Electronic Engineering Radar business Instrumentation |
| Zdroj: | IEEE Sensors Journal. 21:13522-13529 |
| ISSN: | 2379-9153 1530-437X |
| DOI: | 10.1109/jsen.2021.3068388 |
| Popis: | 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 $33\times 31$ 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%. |
| Databáze: | OpenAIRE |
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