A group of researchers at the Massachusetts Institute of Technology has developed a new apparatus that can measure a person’s walking speed better than any other device on the market.
Not only is the new technology effective, it also does not require the person to wear or carry a sensor. Rather, the painting-sized device — known as “WiGait” — can be placed on the wall of a house. From there it analyzes wireless signals reflected off of people’s bodies to monitor numerous behaviors, including breathing and step speed.
That is important because it offers many improvements over current systems. For instance, wearable devices like Fitbit can only look at speed based on step count, while smartphones have proven to be inaccurate while indoors.
“By using in-home sensors, we can see trends in how walking speed changes over longer periods of time,” said lead author Chen-Yu Hsu, a Ph.D. student at the Massachusetts Institute of Technology, according to Yahoo News. “This can provide insight into whether someone should adjust their health regimens, whether that’s doing physical therapy or altering their medications.”
Improved accuracy could be helpful, not just for individual health but for doctors as well. WiGait is able to measure a person’s stride length with 85 to 99 percent accuracy, shedding light on conditions that are characterized by reduced step size, such as Parkinson’s.
Over the past few years, numerous studies have linked walking speed to health issues like the cognitive decline and cardiac diseases. As a result, the new device could help physicians monitor such problems and gain insight into their patient’s health.
“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,” said Ipsit Vahia, a geriatric clinician at McLean Hospital and Harvard Medical School who was not involved in the research, in a statement. “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 human behaviors.”
The team plans to present their paper in May at ACM’s CHI Conference on Human Factors in Computing Systems in Colorado.