Technology Other News 25 Apr 2019 This tool creates pe ...

This tool creates personalised workout recommendations from fitness tracking data

ANI
Published Apr 25, 2019, 11:09 am IST
Updated Apr 25, 2019, 11:09 am IST
Researchers trained FitRec on a dataset of more than 250,000 workout records for over 1,000 runners.
FitRec also is capable of identifying important features that affect workout performance, such as whether a route has hills and the user's level of fitness. (Photo: Pixabay)
 FitRec also is capable of identifying important features that affect workout performance, such as whether a route has hills and the user's level of fitness. (Photo: Pixabay)

A tool called FitRec might estimate heart rates during a workout and recommend routes, researchers have claimed. FitRec is a fitness tracking tool powered by deep learning technology.

The details will be discussed in the meeting, The World Wide Web Conference.

 

Researchers trained FitRec on a dataset of more than 250,000 workout records for over 1,000 runners. This allowed computer scientists to build a model that analysed past performance to predict speed and heart rate given specific future workout times and routes.

FitRec also is capable of identifying important features that affect workout performance, such as whether a route has hills and the user's level of fitness.

The tool can recommend alternate routes for runners who want to achieve a specific target heart rate. It also is capable of making short-term predictions, such as telling runners when to slow down to avoid exceeding their desired maximum heart rate.

"Personalisation is crucial in models of fitness data because individuals vary widely in many areas, including heart rate and ability to adapt to different exercises," said Julian McAuley, a professor.

"The main challenge in building this type of model is that the dynamics of heart rates as people exercise are incredibly complex, requiring sophisticated techniques to model," researchers added.

To build an effective model, computer scientists needed a tool that uses all of the data to learn, but at the same time, can learn personalised dynamics from a small number of data points per user.

Researchers validated FitRec's predictions by comparing data with existing workout records that were not part of the training dataset.

In the future, FitRec could be trained to include other data, such as the way users' fitness levels evolve over time, to make its predictions.

The tool could also be applied to more complex recommendation routes, for example, safety-aware routes.

But in order for the tool to be used in commercial fitness apps, researchers would need to have access to more detailed fitness tracking data and deal with various data quality issues.

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