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Wi-Fi used to monitor respiratory motion

Wi-Fi used to monitor respiratory motion

Technology News |
By Rich Pell



Researchers at the National Institute of Standards and Technology (NIST) have developed a deep learning algorithm that can analyze minuscule changes in Wi-Fi signal interactions within a room to help determine whether someone in the room is struggling to breathe. The algorithm, called BreatheSmart, can work with already available Wi-Fi routers and devices, and was developed to help doctors fight the COVID-19 pandemic.

Previous research had explored using Wi-Fi signals to sense people or movement, but these setups often required custom sensing devices, and data from these studies were very limited.

“As everybody’s world was turned upside down, several of us at NIST were thinking about what we could do to help out,” says Jason Coder, who leads NIST’s research in shared spectrum metrology. “We didn’t have time to develop a new device, so how can we use what we already have?”

Working with the Office of Science and Engineering Labs (OSEL) in the FDA’s Center for Devices and Radiological Health, the researchers advanced a new way to use existing Wi-Fi routers to measure the breathing rate of a person in the room. In Wi-Fi, the “channel state information,” or CSI, is a set of signals sent from the client (such as a cellphone or laptop) to the access point (such as the router).

The CSI signal sent by the client device is always the same, and the access point receiving the CSI signals knows what it should look like. But as the CSI signals travel through the environment, they get distorted as they bounce off things or lose strength. The access point analyzes the amount of distortion to adjust and optimize the link.

These CSI streams are small, less than a kilobyte, so it doesn’t interfere with the flow of data over the channel. The researchers modified the firmware on the router to ask for these CSI streams more frequently, up to 10 times per second, to get a detailed picture of how the signal was changing.

The researchers set up a manikin used to train medical professionals in an anechoic chamber with a commercial off-the-shelf Wi-Fi router and receiver. This manikin is designed to replicate several breathing conditions, from normal respiration to abnormally slow breathing (called bradypnea), abnormally rapid breathing (tachypnea), asthma, pneumonia and chronic obstructive pulmonary diseases, or COPD. 

What alters the Wi-Fi signal is the way the body moves as someone breathes, which varies when wheezing or coughing, compared with breathing normally. As the manikin “breathed,” the movement of its chest altered the path traveled by the Wi-Fi signal. The researchers recorded the data provided by the CSI streams. Although they collected a wealth of data, they still needed help to make sense of what they had gathered.

“This is where we can leverage deep learning,” says Coder.

The researchers worked on a deep learning algorithm to comb through the CSI data, understand it, and recognize patterns that indicated different breathing problems. The algorithm successfully classified a variety of respiratory patterns simulated with the manikin 99.54% of the time. 

“Most of the work that’s been done before was working with very limited data,” says research associate Susanna Mosleh. “We were able to collect data with a lot of simulated respiratory scenarios, which contributes to the diversity of the training set that was available to the algorithm.” 

There has been a lot of interest in using Wi-Fi signals for sensing applications, say the researchers, and they hope that app and software developers can use the process presented in their work as a framework to create programs to remotely monitor breathing. 

“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,” says Coder. “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.”

For more, see “Monitoring Respiratory Motion with Wi-Fi CSI:Characterizing performance and the BreatheSmart Algorithm.”

NIST

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