Wearable sensors can effectively detect walking differences between young and older adults in daily life. With this kind of data gathering research Yuge Zhang aims to improve existing fall prediction models.
“As the aging population continues to grow, more individuals are at risk of gait disorders and falls, which are often underdiagnosed and inadequately evaluated. So fall risk assessment is important for timely prevention. Early detection of individuals at risk for falling, combined with targeted fall prevention training, could help reduce the incidence of these falls.
“Most of the current assessments are based on walking or balance tests in the laboratory. Most falls however happen in daily life environments. Therefore my research focused on using a wearable sensor to analyze gait and assess fall risks, with a particular emphasis on validating existing fall prediction models.
“I found that wearable sensors proof to be very effective. They are portable to capture a wide range of mobility and locomotion during a regular day, making them an ideal tool for continuous monitoring. I also found that an individual’s fall history and depression degrees are robust fall predictors.”
More information on the thesis