In a significant advancement for elder care, researchers at the University of Arizona’s Gutruf Lab have pioneered a wearable device that leverages artificial intelligence to identify early signs of frailty. This innovative approach aims to revolutionize how care for the elderly is administered, moving from a reactive to a preventative model.
“The current model of care is lagging behind,” stated Philipp Gutruf, associate department head of biomedical engineering and senior author of 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 research, detailed in a study published in Nature Communications on December 20, describes a soft mesh sleeve worn on the lower thigh. This device tracks and evaluates leg acceleration, symmetry, and step variability to detect frailty.
Frailty affects 15% of Americans aged 65 and older, making individuals more vulnerable to falls, disabilities, and hospitalizations, as highlighted in a 2015 Journals of Gerontology study.
“This device allows clinicians to intervene early, potentially preventing costly and dangerous outcomes,” added Gutruf.
Design and Technology Integration
Gutruf, who has dedicated seven years at the University of Arizona to developing technology for monitoring biomarkers, previously worked on an adhesive-free wearable that tracks stress indicators through skin gases and water vapor.
The newly developed wearable, a 3D-printed, sensor-lined sleeve approximately two inches wide, is designed to be discreet. By focusing on form and function, the device analyzes motion and provides AI-driven insights. It transmits only the results, reducing data transmission by 99% and eliminating the need for a high-speed internet connection. Data is sent to a smart device via Bluetooth, and the device benefits from long-range wireless charging.
“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,” explained Kevin Kasper, lead study author and biomedical engineering doctoral candidate.
The device is particularly well-suited for remote patient monitoring in rural or under-resourced communities. “We are effectively putting a lab on the patient, no matter where they live,” Kasper noted.
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