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Parametric Convolutional Neural Network for Radar-based Human Activity Classification Using Raw ADC Data
  • Thomas Stadelmayer ,
  • Avik Santra
Thomas Stadelmayer
Infineon Technologies AG, Infineon Technologies AG

Corresponding Author:[email protected]

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Avik Santra
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Radar sensors offer a promising and effective sensing modality for
human activity classification. Human activity classification enables several smart
homes applications for energy saving, human-machine interface for gesture
controlled appliances and elderly fall-motion recognition. Present radar-based
activity recognition system exploit micro-Doppler signature by generating Doppler
spectrograms or video of range-Doppler images (RDIs), followed by deep neural
network or machine learning for classification. Although, deep convolutional neural
networks (DCNN) have been shown to implicitly learn features from raw sensor
data in other fields, such as camera and speech, yet for the case of radar DCNN
preprocessing followed by feature image generation, such as video of RDI or
Doppler spectrogram, is required to develop a scalable and robust classification
or regression application. In this paper, we propose a parametric convolutional
neural network that mimics the radar preprocessing across fast-time and slow-time
radar data through 2D sinc filter or 2D wavelet filter kernels to extract features for
classification of various human activities. It is demonstrated that our proposed
solution shows improved results compared to equivalent state-of-art DCNN solutions
that rely on Doppler spectrogram or video of RDIs as feature images.