Wireless device can see through walls to detect walking speed

May 02, 2017 // By Jean-Pierre Joosting
Breathing, blood pressure, body temperature and pulse provide an important window into the complexities of human health. However, a growing body of research suggests that another vital sign - how fast you walk - could be a better predictor of health issues like cognitive decline, falls, and even certain cardiac or pulmonary diseases.

Unfortunately, it's hard to accurately monitor walking speed in a way that's both continuous and unobtrusive. Professor Dina Katabi's group at MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) have been working on the problem, and believe that the answer is to go wireless.

In a new paper, the team presents "WiGait," a device that can measure the walking speed of multiple people with 95 to 99 percent accuracy using wireless signals. The system is an update of a device that Katabi's team presented to President Obama in 2015.

The size of a small painting, the device can be placed on the wall of a person's house. It builds on Katabi's previous work that analyzes wireless signals reflected off people's bodies to measure a range of behaviors, from breathing and falling to specific emotions. (The signals emit roughly 100 times less radiation than a standard cellphone.)

"By using in-home sensors, we can see trends in how walking speed changes over longer periods of time," says lead author and PhD student Chen-Yu Hsu. "This can provide insight into whether someone should adjust their health regimens, whether that's doing physical therapy or altering their medications."

WiGait is also 85 to 99 percent accurate at measuring a person's stride length, which could allow researchers to better understand conditions like Parkinson's disease that are characterized by reduced step size.

Hsu and Katabi developed WiGait in collaboration with CSAIL PhD student Zachary Kabelac and master's student Rumen Hristov, alongside undergraduate Yuchen Liu from the Hong Kong University of Science and Technology, and assistant professor Christine Liu from the Boston University School of Medicine. The team will present their paper in May at ACM's CHI Conference on Human Factors in Computing Systems in Colorado.