Checkout BreatheSmart, A Deep Learning Algorithm That Uses Wi-fi Signals To Monitor Breathing Patterns

Did you ever imagine that a Wi-Fi signal could determine your respiratory health?

With the growing number of innovations in the field of Artificial Intelligence and Machine Learning, a lot of new research is being carried out, and several algorithms are being instituted to cater to different medical needs. One such deep learning algorithm has been developed by The National Institute of Standards and Technology (NIST) researchers, which can observe the respiratory health of an individual. Named BreatheSmart, the algorithm uses fluctuations in Wi-Fi signals to keep track of a person’s breathing pattern in the vicinity.

A Wi-Fi signal is a wireless communication technology that transmits and receives data between multiple devices using radio waves. The devices, such as a laptop or mobile phone, send a connection request to the Wi-Fi router for connecting to the internet. The signals conveyed by the router are minutely amended by the obstructions such as motions and breathing. As tons of beneficial information can be provided by respiratory indications to deal with unwanted medical circumstances, these minimal environmental changes are being used by BreatheSmart to ascertain if somebody is having trouble breathing.

The Covid-19 period was very serious and scary for many as it caused significant illness and death. The NIST team of researchers has always been concerned about building something to offer a helping hand during a deadly pandemic. Jason Coder, the NIST’s research lead in shared spectrum metrology, shared how the team wanted to develop something from existing technologies to help during the pandemic.

Coder and Susanna Mosleh, a research associate, and the Office of Science and Engineering Labs (OSEL) team, worked on a deep learning algorithm to deduce patterns from the channel state information (CSI) in the Wi-Fi signals. CSI is a set of tiny signals that are less than a kilobyte and are sent from a device to a router. Due to obstacles, these signals lose strength and get altered, followed by which the router examines and enhances the link. To articulately observe how the signal changes, the NIST researchers worked on the firmware on the Wi-Fi router so that the router receives CSI streams up to 10 times per second.

The team monitored how the Wi-Fi signals are affected by the motions in the human body. These motions, such as the chest movement due to slow, fast, or normal respiration, were depicted by a human body model (a manikin). The deep learning algorithm used the motion data yielded from the CSI streams to perceive patterns and trends stipulating breathing problems. The algorithm was able to classify numerous simulated respiratory patterns with an accuracy of 99.54%.

BreatheSmart is unquestionably a very valuable algorithm for Wi-Fi-based respiratory monitoring. It is an effective and low-cost technique to lend support in the health and medical sphere. The ongoing Covid-19 pandemic is all about respiratory issues, and doctors and patients are in dire need of technologies that can help ease the diagnosis. Consequently, BreatheSmart can definitely be utilized for identifying breathing complications and, due to its productive and scalable capability, has high scope in the future.

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Tanya Malhotra is a final year undergrad from the University of Petroleum & Energy Studies, Dehradun, pursuing BTech in Computer Science Engineering with a specialization in Artificial Intelligence and Machine Learning.
She is a Data Science enthusiast with good analytical and critical thinking, along with an ardent interest in acquiring new skills, leading groups, and managing work in an organized manner.

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