Scientists are increasingly looking at predicting health issues such as cognitive decline, cardiac disease, pulmonary disease or future falls via decreases in the patient’s walking speed. And while there are already methods of measuring that speed, some of them can be obtrusive, or don’t provide a true picture of the manner in which the patient typically walks. With that in mind, a team from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) has developed a no-contact system that measures a person’s true walking speed wirelessly.
Currently, the main method of measuring walking speed (aka gait velocity) involves timing the patient with a stopwatch. This causes them to be very aware of the fact that they’re being tested, however, and may result in them walking faster or slower than they would normally.
Devices such as fitness trackers or GPS-enabled smartphones don’t provide very accurate readings, plus the latter won’t work indoors. Another approach is to place a depth-sensing camera in the patient’s home and then analyze footage of them walking, although many patients may find that to be an invasion of their privacy.
That’s where the new “WiGait” system comes in.
Designed by a team led by Prof. Dina Katabi, it’s an extension of technology that was previously developed for MIT’s WiTrack system. It incorporates a flat device that hangs on a wall in the patient’s home, emitting radio signals that contain about one-hundredth the amount of radiation of a standard cellphone.
As the patient walks around their home – even if they’re in another room – the system notes the precise times and locations at which their body reflects back the signals that are sent out. By analyzing that data, it is able to determine their walking speed (and any changes in it) with a claimed accuracy of 95 to 99 percent. It’s also 85 to 99 percent accurate at measuring their stride length, which is known to decrease due to conditions such as Parkinson’s disease.
Every Wednesday is Wearable Wednesday here at Adafruit! We’re bringing you the blinkiest, most fashionable, innovative, and useful wearables from around the web and in our own original projects featuring our wearable Arduino-compatible platform, FLORA. Be sure to post up your wearables projects in the forums or send us a link and you might be featured here on Wearable Wednesday!
Adafruit publishes a wide range of writing and video content, including interviews and reporting on the maker market and the wider technology world. Our standards page is intended as a guide to best practices that Adafruit uses, as well as an outline of the ethical standards Adafruit aspires to. While Adafruit is not an independent journalistic institution, Adafruit strives to be a fair, informative, and positive voice within the community – check it out here: adafruit.com/editorialstandards
Stop breadboarding and soldering – start making immediately! Adafruit’s Circuit Playground is jam-packed with LEDs, sensors, buttons, alligator clip pads and more. Build projects with Circuit Playground in a few minutes with the drag-and-drop MakeCode programming site, learn computer science using the CS Discoveries class on code.org, jump into CircuitPython to learn Python and hardware together, TinyGO, or even use the Arduino IDE. Circuit Playground Express is the newest and best Circuit Playground board, with support for CircuitPython, MakeCode, and Arduino. It has a powerful processor, 10 NeoPixels, mini speaker, InfraRed receive and transmit, two buttons, a switch, 14 alligator clip pads, and lots of sensors: capacitive touch, IR proximity, temperature, light, motion and sound. A whole wide world of electronics and coding is waiting for you, and it fits in the palm of your hand.
Have an amazing project to share? The Electronics Show and Tell is every Wednesday at 7:30pm ET! To join, head over to YouTube and check out the show’s live chat and our Discord!
Python for Microcontrollers – Adafruit Daily — Python on Microcontrollers Newsletter: CircuitPython Comes to the ESP32-P4, Emulating Arm on RISC-V, and Much More! #CircuitPython #Python #micropython @ThePSF @Raspberry_Pi
EYE on NPI – Adafruit Daily — EYE on NPI Maxim’s Himalaya uSLIC Step-Down Power Module #EyeOnNPI @maximintegrated @digikey