Human motion classification with micro-Doppler radar and Bayesian-optimized convolutional neural networks
In recent years, Doppler radar has emerged as an alternative sensing modality for human
gait classification since it measures not only the target speed, but also the local dynamics of
the moving body parts, thereby creating a unique spectral signature. This paper presents a
learning-based method for classifying human motions from micro-Doppler signals. Inspired
by the applications of deep learning, the proposed method extracts features from the time-
frequency representation of the radar signal using a cascaded of convolutional network …
gait classification since it measures not only the target speed, but also the local dynamics of
the moving body parts, thereby creating a unique spectral signature. This paper presents a
learning-based method for classifying human motions from micro-Doppler signals. Inspired
by the applications of deep learning, the proposed method extracts features from the time-
frequency representation of the radar signal using a cascaded of convolutional network …
[引用][C] Human Motion Classification with Micro-Doppler Radar and Bayesian-Optimized Convolutional Neural Networks
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