Multiple wearable sensors predict illness


Stanford’s Michael Snyder has published the results of a health wearable study, in which 2 billion measurements were taken from 60 subjects, concluding that such devices can be used to predict illness.

Continuous biosensor data, plus blood chemistry, gene expression and other tests,  were included. Participants wore 1-7 commercial wearables, which collected more than 250,000 measurements per day. Weight, heart rate, blood oxygen level, skin temperature, sleep, walking, biking and running, calories expended, acceleration, and exposure to gamma rays and X-rays were analyzed.

To individualize the process, both baseline and illness values were established for each person. It was possible to monitor deviations from normal, and associate those deviations with environmental, illness or other factors that affect health.  Deviation patterns correlated with specific health problems.  (The lead author was able to detect Lyme Disease in himself during the study.) Algorithms which spot these patterns could be used for future diagnostics or research.

ApplySci’s 6th  Digital Health + NeuroTech Silicon Valley  –  February 7-8 2017 @ Stanford   |   Featuring:   Vinod Khosla – Tom Insel – Zhenan Bao – Phillip Alvelda – Nathan Intrator – John Rogers – Roozbeh Ghaffari –Tarun Wadhwa – Eythor Bender – Unity Stoakes – Mounir Zok – Sky Christopherson – Marcus Weldon – Krishna Shenoy – Karl Deisseroth – Shahin Farshchi – Casper de Clercq – Mary Lou Jepsen – Vivek Wadhwa – Dirk Schapeler – Miguel Nicolelis