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

Human Activity Classification Based on Point Clouds Measured by Millimeter Wave MIMO Radar with Deep Recurrent Neural Networks
Kim, YoungwookAlnujaim, IbrahimOh, Daegun
DGIST Authors
Kim, YoungwookAlnujaim, IbrahimOh, Daegun
Issued Date
Author Keywords
deep convolutional neural networksdeep recurrent neural networksFeature extractionFMCW radarHuman activity classificationMillimeter wave radarMIMO radarpoint cloudsRadarRadar antennasRadar measurementsShapeThree-dimensional displays
ConvolutionConvolutional neural networksDeep neural networksMillimeter wavesMIMO radarRadar measurementAngular resolutionClassification accuracyConvolutional networksHuman activitiesHuman subjectsTime instancesTime varyingVirtual arrayRecurrent neural networks
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 33×31 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%. IEEE
Institute of Electrical and Electronics Engineers
Related Researcher
  • 오대건 Oh, Daegun 지능형로봇연구부
  • Research Interests
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