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2010 Grants - Hirth
Fall Recognition Using Environmental Electronic Sensors
Victor Hirth, M.D.
Columbia, South Carolina
2010 Everyday Technologies for Alzheimer's Care
Falls and fall injuries continue to be major safety challenges for older adults with dementia. In the long-term care setting, falls and near falls often go unreported, leading to missed opportunities for interventions that might prevent potentially serious and disabling falls. Fall detection systems have been developed and tested in laboratory settings using manikins, few studies have been conducted in "real-world" settings.
Victor Hirth, M.D., proposes to test the feasibility and acceptability of implementing a fall detection system in "real world" settings of long- term care, assisted living and independent living environments. The system monitors floor vibrations created by sudden impacts, combined with motion sensors to accurately detect falls. The study aims to validate the system against incident reports of falls with a goal of greater than 90 percent of incident report falls detected by the system.
The system will ideally be able to predict incipient falls based on floor vibration and motion events prior to an actual fall, providing the opportunity to intervene prior to a fall event.