Why running wearables fail: behind the scenes of our smartwatch demo
Last week we launched our smartwatch demo app that demonstrates how our technology can enable extraordinary gains in accuracy when embedded into standard wearables - to prove it we ran two demos on a track and on a road course.
We wanted to peel back the technology and tell the story of what’s going on inside those tests - and why regular wearables can be so inaccurate in recording position and path.
Our Senior Software Engineer, Henry, track- and road tested the technology in two ways:
Test #1: Accurate distance travelled
For this, we simply ran a standard 400m track at Battersea Athletics Track. This test shows the side-by-side results from a Fossil Gen 5 watch - the GNSS data that the watch uses, against the result that it can achieve using our D-Tail technology.
On a track which is fairly well exposed to the open sky you would expect that you might get a good GNSS signal however, the surrounding trees and a small metal-clad grandstand may have obscured some satellites signals or created reflected signals.
In addition, despite saying that it had a fix, the watch may not have acquired enough satellites to provide accurate positioning from GNSS alone. In cases like this, FocalPoint's Human Motion Model enables us provide a more accurate trace of the body's motion through space.
Finally, the GNSS antenna inside the watch is usually smaller than you would expect to have in a smartphone or in-car satnav, making it less sensitive, and sometimes the dynamics of running itself, and the shocks and accelerations on the device can impact the signal to noise ratios of the received signals.
All told, it's no surprise that using standard GNSS alone, the watch only managed a distance reading of 473m for a lap of a 400m track. By comparison, using FocalPoint's technology, it measured 396m
Test #2: Accurate line on a map
For this, we ran through the streets of Clerkenwell in London to demonstrate how our Human Motion Model can accurately capture movement to a cm-level, even in GPS-denied spaces; and how we can filter out bad GPS fixes to increase the accuracy of the path.
When you zoom into the route, you can quite clearly see the reasons why current sports wearables struggle to provide accurate view of the route that you have run:
It's clear from the infographic above that the architecture and environment of modern life makes it harder for GNSS receivers to achieve an accurate fix, creating errors from the wearables and smartphones that they're inside of.
It's not just cities and urban canyons either: Geographic features and even trees can obscure or reflect GNSS signals too - meaning that a run in the countryside in the winter versus in the summer could return significantly different distance and route readings too.
We’d love to hear your feedback on the demo. Perhaps you have some other wearable fails which you can share with us? Please tag us in your social media posts so we share them.
Fellow runners - help share our demo film on Instagram and Twitter.
Manufactures/Developers - request a demo here.
To learn more about the above use case, please head to our Running page.