IEOR Graduate Students Creates New Exercise App Using Machine Learning


IEOR graduate students Mo Zhou and Yonatan Mintz are addressing exercise commitment issues with a new app that uses machine learning to adjust goals on a daily basis.

Each New Year around 20% of Americans vow to lose weight, eat healthier, or exercise more. However, for many, this New Year’s resolution is one of the most difficult to keep. Work, school, and daily stress can make it difficult to stay on track to meeting fitness goals. Setting unchanging goals (e.g. “walk 10,000 steps daily”) can affect morale and may even reduce chances of getting fit.

“Some exercise apps automate exercise goals […] but they can’t adapt to an individual’s success [or lack of thereof]. As a result, the goals can get out of sync with the actual performance,” said IEOR Professor Anil Aswani who oversaw the research. 

Some exercise apps do have goals that adapt based on the user’s results, usually through the use of human coaches. However, because human input is typically more expensive than an automated system, the cost of Zhou’s app is significantly lower than others on the market that utilize human support. 

The app was tested in a controlled experiment with 64 participants. While all participants did not perform as well as they originally hoped, the participants that used Zhou’s app logged 10% more steps on average than the control group. The results were positive. Zhou hopes to take America into a healthier future one step at a time. 

Read the full story at Berkeley Engineering

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