Researchers at the University of California San Diego's Jacobs School of Engineering have developed a fitness tool that utilizes deep learning to analyze runners' heart rates and use them to recommend the healthiest routes.
UCSD computer scientists trained the FitRec program using data from 1,000 runners' more than 250,000 total workouts. The algorithm is able to analyze the impact of certain routes' terrain and lengths, and use past speed and heart rate measures to predict future performance; FitRec can then recommend specific routes to help runners achieve a target heart rate or avoid exceeding a desired maximum rate.
The tool is powered using a type of deep learning called long short-term memory networks, which produces a model that is built on a vast set of previous, more generalized data, but is also capable of analyzing, learning from and making predictions based on a much smaller set of hyper-personalized data from a single user.
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