Sovrinti Inc. awarded a research grant for the development of Artificial Intelligence (AI) techniques that predict adverse health events in senior populations.
— John Fitch, Sovrinti Principal and CTO
WACO, TX, UNITED STATES, June 14, 2023/EINPresswire.com/ — Waco based Sovrinti Inc. has been awarded a research grant for the development of Artificial Intelligence (AI) techniques that predict adverse health events in senior populations using Sovrinti’s in-home sensing data. The award was made by the Johns Hopkins AI & Technology Collaboratory for Aging Research (JH AITC), one of three research centers within the Artificial Intelligence and Technology Collaboratories (AITC) for Aging Research program funded by the National Institute on Aging, part of the National Institutes of Health. The Sovrinti project is one of eight studies selected by JH AITC during the first year of the AITC program.
Previously developed with support from the National Institute on Aging, the Sovrinti system uses a patented set of home sensors to identify Activities of Daily Living (ADLs) and look for changes from individual routines. Subtle changes in behavior patterns are mapped to cognitive and physiological relevant criteria allowing early identification of rising risk for acute events. Without any additional effort from the senior or the caregiver, the Sovrinti system uses the power of smart home devices and data analytics to identify specific areas requiring care team attention before a health situation becomes acute and costly.
“The goal of our system is to move from a reactive to a predictive care approach using the same senior care management structure of ADLs that has been used for decades” says John Fitch, Sovrinti Principal and CTO. “We look forward to collaborating with Johns Hopkins on both the technical and business development side as it provides a world class opportunity to continue moving this technology into the mainstream of senior care health management.”
The research program will develop and train algorithms using an existing Sovrinti data set to identify changes in ADL patterns up to 2 months in advance of acute health events. The existing data is comprised of more than 1500 months of attributed activity and adverse event data from over 120 seniors and their care givers in both homes and assisted living environments. Examples might include changes in bath rooming habits, mobility patterns, and sleep patterns from an arising urinary tract infection (UTI), a well-documented precursor to cognitive changes and rising fall risks as it progresses. In this example, the ability to identify a potential UTI early allows for low-cost diagnostic and treatment procedures versus reacting to a fall with its potential resultant injuries.
The Sovrinti system utilizes smart home technology and ADL identification algorithms developed by sister company Birkeland Current. Funds to support this AITC study were provided by the Johns Hopkins University AITC under award number P30AG073104. Research described in this announcement was supported by National Institute on Aging grants P30AG073104 and R44AG065118B. The content is solely the responsibility of the authors and does not necessarily represent the views of the National Institutes of Health.
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