Passive Localization for Comparing Physical Activities in Indoor Environments

Device-free passive localization can be used for detecting physical activity being exhibited by an individual in a typical indoor environment, only by examining the variations in wireless signal strength caused by that activity. This paper uses machine learning classifiers to distinguish between four physical activities performed by an individual in a controlled indoor setting. The activities of interest include two diagonal walking movements in opposite directions, and two similar movements culminating in the individual abruptly stopping to emulate a fall. It has been shown in this paper that an analysis of variations in signal strength can accurately distinguish between the concerned physical activities. This paper is a step towards passively and non-intrusively detecting whether an individual has fallen down in an indoor environment.