WiGait detects walking speed with wireless
May 1, 2017
We’ve long known that blood pressure,
breathing, body temperature and pulse provide an important window into
the complexities of human health. But 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)
has been working on the problem, and believes that the answer is to go
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 size of a small painting, the device can be placed on the wall of a
person’s house and its signals emit roughly one-hundredth the amount of
radiation of a standard cellphone. It builds on Katabi’s previous work
on WiTrack, which analyzes wireless signals reflected off people’s
bodies to measure a range of behaviors from breathing and falling to
“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 regimen, 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 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.
How it works
Today, walking speed is measured by physical therapists or clinicians
using a stopwatch. Wearables like FitBit can only roughly estimate speed
based on step count, and GPS-enabled smartphones are similarly
inaccurate and can’t work indoors. Cameras are intrusive and can only
monitor one room. VICON motion tracking is the only method that’s
comparably accurate to WiGate, but it is not widely available enough to
be practical for monitoring day-to-day health changes.
Meanwhile, WiGait measures walking speed with a high level of
granularity, without requiring that the person wear or carry a sensor.
It does so by analyzing the surrounding wireless signals and their
reflections off a person’s body. The CSAIL team’s algorithms can also
distinguish walking from other movements, such as cleaning the kitchen
or brushing one's teeth.
Katabi says the device could help reveal a wealth of important health
information, particularly for the elderly. A change in walking speed,
for example, could mean that the person has suffered an injury or is at
an increased risk of falling. The system's feedback could even help the
person determine if they should move to a different environment such as
an assisted-living home.
avoidable hospitalizations are related to issues like falls, congestive
heart disease, or chronic obstructive pulmonary disease, which have all
been shown to be correlated to gait speed,” Katabi says. “Reducing the
number of hospitalizations, even by a small amount, could vastly improve
health care costs.”
The team developed WiGait to be more privacy-minded than cameras,
showing you as nothing more than a moving dot on a screen. In the future
they hope to train it on people with walking impairments from
Parkinson’s, Alzheimer’s or multiple sclerosis, to help physicians
accurately track disease progression and adjust medications.
“The true novelty of this device is that it can map major metrics of
health and behavior without any active engagement from the user, which
is especially helpful for the cognitively impaired,” says Ipsit Vahia, a
geriatric clinician at McLean Hospital and Harvard Medical School, who
was not involved in the research. “Gait speed is a proxy indicator of
many clinically important conditions, and down the line this could
extend to measuring sleep patterns, respiratory rates, and other vital