Monitoring breathing via signal strength in wireless networks

pre-printThis paper shows experimentally that standard wireless networks which measure received signal strength (RSS) can be used to reliably detect human breathing and estimate the breathing rate, an application we call "BreathTaking". We present analysis showing that, as a first order approximation, breathing induces sinusoidal variation in the measured RSS on a link, with amplitude a function of the relative amplitude and phase of the breathing-affected multipath. We show that although an individual link may not reliably detect breathing, the collective spectral content of a network of devices reliably indicates the presence and rate of breathing. We present a maximum likelihood estimator (MLE) of breathing rate, amplitude, and phase, which uses the RSS data from many links simultaneously. We show experimental results which demonstrate that reliable detection and frequency estimation is possible with 30 seconds of data, within 0.07 to 0.42 breaths per minute (bpm) RMS error in several experiments. The experiments also indicate that the use of directional antennas may improve the systems robustness to external motion

Breathfinding: A Wireless Network that Monitors and Locates Breathing in a Home

This paper explores using RSS measurements on many links in a wireless network to estimate the breathing rate of a person, and the location where the breathing is occurring, in a home, while the person is sitting, laying down, standing, or sleeping. The main challenge in breathing rate estimation is that "motion interference", i.e., movements other than a person's breathing, generally cause larger changes in RSS than inhalation and exhalation. We develop a method to estimate breathing rate despite motion interference, and demonstrate its performance during multiple short (3-7 minute) tests and during a longer 66 minute test. Further, for the same experiments, we show the location of the breathing person can be estimated, to within about 2 m average error in a 56 square meter apartment. Being able to locate a breathing person who is not otherwise moving, without calibration, is important for applications in search and rescue, health care, and security

Energy efficient radio tomographic imaging

pre-printIn this paper, our goal is to develop approaches to reduce the energy consumption in Radio Tomographic Imaging (RTI)-based methods for device free localization without giving up localization accuracy. Our key idea is to only measure those links that are near the current location of the moving object being tracked. We propose two approaches to find the most effective links near the tracked object. In our first approach, we only consider links that are in an ellipse around the current velocity vector of the moving object. In our second approach, we only consider links that cross through a circle with radius r from the current position of the moving object. Thus, rather than creating an attenuation image of the whole area in RTI, we only create the attenuation image for effective links in a small area close to the current location of the moving object. We also develop an adaptive algorithm for determining r. We evaluate the proposed approaches in terms of energy consumption and localization error in three different test areas. Our experimental results show that using our approach, we are able to save 50% to 80% of energy. Interestingly, we find that our radius-based approach actually increases the accuracy of localization