Predicting blood pressure levels using personal data

Using wearable off-the-shelf technology and machine learning, it’s possible to predict an individual's blood pressure and provide personalized recommendations to lower it based on this data



Researchers from Mobile Systems Design Lab in the Department of Electrical and Computer Engineering at UC San Diego's Jacobs School of Engineering, collected sleep, exercise and blood pressure data from eight patients over 90 days using a FitBit Charge HR and Omron Evolv wireless blood pressure monitor. Using machine learning and this data from prevailing wearable devices, they developed an algorithm to predict the users' blood pressure and point out which particular health behaviors affected it the most.


This research was conducted as part of the Center for Wireless Communications' Connected Health program, supported by industry partners including Kaiser Permanente, UC San Diego Health, Samsung Digital Health and Teradata.


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