University of Arizona researchers in the Gutruf Lab have developed a comfortable, easy-to-use wearable device that incorporates artificial intelligence to detect subtle warning signs of frailty, signifying a leap forward in elderly care.
"The current model of care is lagging behind," said Philipp Gutruf, associate department head of biomedical engineering and senior author on the study. "Right now, we often wait for a fall or hospitalization before we assess a patient for frailty. We wanted to shift the paradigm from reactive to preventative."
The project study , published in Nature Communications on Dec. 20, introduces a soft mesh sleeve worn around the lower thigh that monitors and analyzes leg acceleration, symmetry and step variability.
Frailty, which indicates greater susceptibility to falls, disabilities and hospitalization, affects 15% of U.S. residents 65 and older, according to a 2015 study in the Journals of Gerontology .
"This device allows clinicians to intervene early, potentially preventing costly and dangerous outcomes," said Gutruf.
Form and function define design
The associate professor has spent the last seven years at the U of A developing technology that monitors biomarkers. His lab published a study in May on an adhesive-free wearable that measures water vapor and skin gases to track signs of stress.
Adapting and expanding on that technology, the approximately two-inch-wide, 3D-printed sleeve lined with tiny sensors is "designed to be invisible," said Gutruf.
The sleeve simultaneously records and analyzes motion of the wearer and produces an AI analysis. With the device sending just the results, not the actual hundreds of hours of recorded data, transmission is reduced by 99% and the need for high-speed internet is eliminated. Results are transferred via Bluetooth to a smart device. And long-range wireless charging capabilities free the user from plugging in the device or swapping out a battery.
"Continuous, high-fidelity monitoring creates massive datasets that would normally drain a battery in hours and require a heavy internet connection to upload. We solved this with Edge AI," said Kevin Kasper, lead study author and biomedical engineering doctoral candidate.
The AI-enabled technology is "an ideal solution for remote patient monitoring in rural or under-resourced communities," he added.
"We are effectively putting a lab on the patient, no matter where they live."
