Sensor System Predicts & Prevents Falls
A sensor system developed by researchers at the University of Missouri can measure gait speed and stride length to analyse walking patterns and help predict whether a person is at risk of falling up to three weeks in advance.
The technology aims to improve co-ordinated healthcare for older adults by assisting nurses in assessing functional decline, providing treatment and preventing falls. Analysing data collected from the camera-based systems at a retirement residence in Columbia, Missouri, researchers found that a gait speed decline of 5cm per second correlated to an 86.3% probability of falling within the following three weeks. The system, which tracks changes in the data, sends automated alert emails to nurses when irregular motion is detected.
Research into the system’s effectiveness released in August found that by integrating care co-ordination and sensor technology, residents whose data was tracked were able to live independently for an average of four years, compared to the US national average of 22 months.
"For many older adults, the risk of falling impacts how long seniors can remain independent," said Marilyn Rantz, professor emerita of nursing at the University of Missouri’s Sinclair School of Nursing. "Being able to predict that a person is at risk of falling will allow caretakers to intervene with the necessary care to help seniors remain independent as long as possible."
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