[Institution of Engineering and Technology 12th European Conference on Antennas and Propagation (EuCAP 2018) - London, UK (9-13 April 2018)] 12th European Conference on Antennas and Propagation (EuCAP 2018) - Remote Vital Sign Recognition through Machine Learning augmented UWB

摘要： This paper describes an experimental demonstration of machine learning (ML) techniques supplementing radar to distinguish and detect vital signs of users in a domestic environment. This work augments an intelligent location awareness system previously proposed by the authors. That research employed Ultra-Wide Band (UWB) radar complemented by supervised machine learning techniques to remotely identify a person’s room location via ?oor plan training and time stamp correlations. Here, the remote breathing and heartbeat signals are analyzed through Short Term Fourier Transformation (STFT) to determine the Micro-Doppler signature of those vital signs in different room locations. Then, Multi-Class Support Vector Machine (MC-SVM) is implemented to train the system to intelligently distinguish between vital signs during different activities. Statistical analysis of the experimental results supports the proposed algorithm. This work could be used to further understand, for example, how active older people are by engaging in typical domestic activities.