Remote Vital Sign Recognition Through Machine Learning Augmented UWB
Dudley, S, Rana, S., Dey, M, Brown, R and Siddiqui, H (2018). Remote Vital Sign Recognition Through Machine Learning Augmented UWB. European Conference on Antennas and Propagation. Excel London, Docklands 09 - 13 Apr 2018 London South Bank University.
|Authors||Dudley, S, Rana, S., Dey, M, Brown, R and Siddiqui, H|
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 persons room location via floor 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.
|Keywords||Terms—Indoor Positioning System (IPS); Breathing; Heartbeat; Ultra-Wide Band (UWB); Short Term Fourier Transform (STFT); Multi-Class Support Vector Machine (MCSVM)|
|Publisher||London South Bank University|
|Accepted author manuscript|
CC BY 4.0
|09 Apr 2018|
|Publication process dates|
|Deposited||17 Feb 2018|
|Accepted||18 Dec 2017|
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