New Device Measuring Walking Speed Using Wireless Signals


A new tracking device is developed to track the speed and the manner of waking. By wireless signals and can detect cardiac problems in people. 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 wireless.

The device named as WiGait. It 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 placed on the wall of a person’s house. Its signals emit roughly one-hundredth the amount of radiation of a standard cellphone. It builds on Katabi’s previous work on WiTrack analyzes wireless signals reflected off people’s bodies. To measure a range of behaviors from breathing and falling to specific emotions.

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WiGait is also 85 to 99 percent accurate at measuring a person’s stride length. It allows researchers to better understand conditions like Parkinson’s disease characterized by reduced step size.

How it works

Moreover, walking speed measured by physical therapists or clinicians using 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 WiGait. 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.

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The device could help reveal a wealth of important health information, particularly for the elderly. A change in walking speed 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.

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.