VTT aims to take the accuracy of wearable activity trackers to a new level


With the explosion in popularity of wearable activity trackers in recent years, people have access to a huge variety of stats and analysis tools to help them set personal goals and track their performance. But there is a problem: many of these devices are way off the mark when it comes to accuracy. According to the experts at the Finnish research centre VTT, the answer to this challenge lies in the algorithms that form the backbone of the software in these devices; improve the number crunching and you can provide users with a smart solution that not only accurately tracks activity, but also learns as it goes along.

The problem with many of today’s commercially available wearable activity trackers is that the algorithms they use to process the activity data are based on general population data. While you might be able to enter a limited set of parameters such as height, weight, and age, there is often no opportunity to enter parameters that are highly personal, such as gait pattern, which also varies according to the shoes you are wearing and the type of ground you are walking or running on.
And it is this sort of data that makes a real difference when it comes to presenting accurate results for your activity.

The issue until now has been one of size – the more accurate the algorithm, the more ‘space’, or computing power, it needs. “Device manufacturers have been faced with a trade-off between memory size, processing requirements, and performance in the past,” says Jani Mäntyjärvi, principal scientist at VTT. “It’s a matter of the design requirements, tailoring the approach to your needs. You have to consider the activities you want to recognise and define, the number of parameters – in other words the size of the algorithm – and the performance level you’re looking for.”

“Many of the commercially available wearables just don’t have the accuracy you need to give a true picture of the calories you’re burning or the movements you’re making,” adds Mäntyjärvi. “They can be as much as 40% off, and even the best devices currently available probably have an error margin of somewhere in the region of 15%. So while you might think you’ve nailed that calorie target on your run or cycle ride, the reality is probably quite different. Accuracy of close to 100% is possible, but you’d need tens of thousands of parameters and a lot more computing power than today’s wearable devices can pack in.”

What is needed is a deeply intelligent algorithm that can build up a picture of movements based on some simple activity prompts and then use this to generate much more accurate results. VTT has already taken significant steps towards solving the size vs. performance problem with a new approach based on deep neural networks. The Deep Activity algorithm developed at VTT can be partly based on general population data and integrate machine learning to create a personalised movement model. It can also start completely from scratch, learning as it goes along without using any general data as a basis.

“With existing algorithms the device makes a lot of assumptions based on what applies to the average person, whereas with Deep Activity it’s like starting with a blank slate,” says Mäntyjärvi. “The algorithm ‘learns’ your personal movement patterns in a matter of just a few minutes, and gets smarter all the time. Some patterns can even be recognised without any input from the user at all.”

Jani Mäntyjärvi sees wearables for tracking activity – whether for sport or health-on-the-move related applications – taking huge leaps forward in the near future. “Technologies like augmented and mixed reality will open up a whole new world of possibilities in the coming years. In a year or two from now, pretty much every activity-tracking application we download to our smartphone or wearable will include some form of AI element,” he points out.

Caption: A Deep Activity bracelet (photo: VTT)

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