TITLE: A Dataset of Human Motion Status Using IR-UWB Through-wall Radar 

ABSTRACT: Ultra-wideband (UWB) through-wall radar has a wide range of applications in
non-contact human information detection and monitoring. With the integration of
machine learning technology, its potential prospects include the physiological
monitoring of patients in the hospital environment and the daily monitoring at
home. Although many target detection methods of UWB through-wall radar based on
machine learning have been proposed, there is a lack of an opensource dataset
to evaluate the performance of the algorithm. This published dataset was
measured by impulse radio UWB (IR-UWB) through-wall radar system. Three test
subjects were measured in different environments and several defined motion
statuses. Using the presented dataset, we propose a human-motion-status
recognition method using a convolutional neural network (CNN), the detailed
dataset partition method and recognition process flow is given. On the
well-trained network, the recognition accuracy of testing data for three kinds
of motion statuses is higher than 99.7%. The dataset presented in this paper
considers a simple environment. Therefore, we call on all organizations in the
UWB radar field to cooperate to build opensource datasets to further promote
the development of UWB through-wall radar.
Ultra-wideband (UWB) through-wall radar has a wide range of applications in
non-contact human information detection and monitoring. With the integration of
machine learning technology, its potential prospects include the physiological
monitoring of patients in the hospital environment and the daily monitoring at
home. Although many target detection methods of UWB through-wall radar based on
machine learning have been proposed, there is a lack of an opensource dataset
to evaluate the performance of the algorithm. This published dataset was
measured by impulse radio UWB (IR-UWB) through-wall radar system. Three test
subjects were measured in different environments and several defined motion
statuses. Using the presented dataset, we propose a human-motion-status
recognition method using a convolutional neural network (CNN), the detailed
dataset partition method and recognition process flow is given. On the
well-trained network, the recognition accuracy of testing data for three kinds
of motion statuses is higher than 99.7%. The dataset presented in this paper
considers a simple environment. Therefore, we call on all organizations in the
UWB radar field to cooperate to build opensource datasets to further promote
the development of UWB through-wall radar.
A Dataset of Human Motion Status Using IR-UWB Through-wall Radar
http://arxiv.org/abs/2008.13598v1