How you walk can foreshadow if you’ll fall.
Researchers from the Sinclair School of Nursing and the College of Engineering at the University of Missouri found that embedded sensors measuring in-home gait speed and stride length can predict likely falls. Their findings have been published in the Western Journal of Nursing Research.
“We have developed a sensor system that can measure walking patterns in the home, including gait speed and stride length,” says Marjorie Skubic, PhD, director of the MU Center for Eldercare and Rehabilitation Technology and professor of electrical and computer engineering in a press release. “Assessment of these functions through the use of sensor technology is improving coordinated healthcare for older adults.”
Researchers analyzed a decade of data collected from sensor systems installed in independent living apartments at TigerPlace by Americare and the Sinclair School of Nursing in Columbia, Missouri. The sensors detected pre-fall changes in residents’ Kinect-recorded gait parameters.
Researchers found a cumulative change in speed over time is associated with the probability of a fall. A gait speed decline of 5 centimeters per second was associated with an 86.3 percent probability of falling within the following three weeks. In addition, shortened stride length was associated with a 50.6 percent probability of falling within the next three weeks.
Future sensor research will focus on how nurses can best use the fall prediction statistics to intervene before the fall occurs, allowing elders to remain independent as long as possible. The National Institutes of Health supported the research.