Human Motion Classification with Micro-Doppler Radar and Bayesian-Optimized Convolutional Neural Networks

Authors

Thanh Le Hoang, University of WollongongFollow
Son Lam Phung, University of WollongongFollow
Abdesselam Bouzerdoum, University of WollongongFollow
Fok Hing Chi Tivive, University of WollongongFollow

RIS ID

130861

Publication Details

H. Le, S. Phung, A. Bouzerdoum & F. Tivive, "Human Motion Classification with Micro-Doppler Radar and Bayesian-Optimized Convolutional Neural Networks," in IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP): Proceedings, 2018, pp. 2961-2965.

Abstract

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 layers. To design a optimal network architecture, the Bayesian optimization with Gaussian process priors is employed. Experimental results on real data are presented, which show a significant improvement compared to three existing approaches.