An algorithm can use WiFi signal changes to help identify breathing issues

NIST says it works with existing routers and devices.

R. Jacobson/NIST

National Institute of Standards and Technology (NIST) researchers have developed a way to monitor breathing based on tiny changes in WiFi signals. They say their BreatheSmart deep-learning algorithm could help detect if someone in the household is having breathing issues.

WiFi signals are almost ubiquitous. They bounce off of and pass through surfaces as they try to link devices with routers. But any movement will alter the signal's path, including how the body moves as we breathe, which can change if we have any issues. For instance, your chest will move differently if you're coughing.

Other researchers have explored the use of WiFi signals to detect people and movements, but their approaches required dedicated sensing devices and their studies provided limited data. A few years ago, a company called Origin Wireless developed an algorithm that works with a WiFi mesh network. Similarly, NIST says BreatheSmart works with routers and devices that are already available on the market. It only requires a single router and connected device.

The scientists changed the firmware on a router so that it would check "channel state information,” or CSI, more frequently. CSI refers to the signals that are sent from a device, such as a phone or laptop, to the router. CSI signals are consistent and the router understands what they should look like, but deviations in the environment, such as the signal being affected by surfaces or movement, modify the signals. The researchers got the router to request these CSI signals up to 10 times per second to gain a better sense of how the signal was being modified.

The team simulated several breathing conditions with a manikin and monitored changes in CSI signals with an off-the-shelf router and receiving device. To make sense of the data they collected, NIST research associate Susanna Mosleh developed the algorithm. In a paper, the researchers noted that BreatheSmart correctly identified the simulated breathing conditions 99.54 percent of the time.

Mosleh and Jason Coder, who heads up NIST’s research in shared spectrum metrology, hope developers will be able to use their research to create software that can remotely monitor a person's breathing with existing hardware. “All the ways we’re gathering the data is done on software on the access point (in this case, the router), which could be done by an app on a phone,” Coder said. “This work tries to lay out how somebody can develop and test their own algorithm. This is a framework to help them get relevant information.”