More than 50% of the whole world lives with chronic diseases leading to a global economic burden of 47 trillion dollars. Healthcare organizations are moving towards managing patients outside hospital, thereby improving patient safety and quality of life. Current at-home ambulatory remote monitoring analytics based on population level thresholds of individuals physiology have shown poor outcomes and high degree of false alarm burden. The personalized multivariate physiology analytics leverages readily-available low-cost wearable biosensors to detect subtle physiology changes precursor of patient's health deterioration. In this paper we present a novel personalized multivariate physiology analytics for remote patient monitoring in an ambulatory setting. We also present our verification and validation results using perturbation testing along with clinical trial results.

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http://dx.doi.org/10.1109/EMBC.2019.8856482DOI Listing

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