R42AG059475

Project Summary and Abstract Falls are the leading cause of death due to injury. Falls are so common that 30% of community dwelling older adults, and 50% of residents in Care Facilities will experience a fall in the coming year. The risk of falling substantially increases for those having Alzheimer’s disease and related dementias. The financial burden is significant with fall-related costs being $50 billion. Care Facilities, who are often liable for the well-being of their patients, bear a substantial portion of the cost. A fall can cost $10,484 per case for Care Facilities. Commercially available fall detection systems operate via wearable pendant-based devices that patients press after experiencing a fall. Newer generations of these systems also incorporate accelerometers that are reportedly able to detect falls. These systems are patient-dependent, meaning that a patient must be wearing the pendant for it to work which older adults, particularly those with cognitive impairments, often do not. Furthermore, the patient has to be cognizant to press the button to call for aid if the pendant does not activate during a fall. This is unlikely to occur as even when people are not cognitively impaired, they will only activate the system 20% of the time. There is a clear need for an automated, patient-independent fall detection system to fill the gaps left by current approaches. Better yet would be a system that can detect non-injurious falls or changes in gait parameters, both of which are predictors of oncoming injurious falls. ASSET, in partnership with the University of South Carolina, has developed a patented, floor vibration monitoring system that can detect falls and collect gait information whilst being patient independent. The innovative product has the ability to firmly place control of liability back into the hands of Care Facilities much like what a fire alarm does for property damage from fires, and potentially saving ~$2.2 billion in fall-related costs with just 5% market adoption. During Phase II our overall goals are two-fold, first to further develop a system that does not rely on the patient to operate, overcoming the limitation of wearable systems and can additionally capture falls that are a predictor of oncoming injurious falls. We will monitor common areas with our vibration sensor system in places where Care Facility staff report the majority of falls occur. To accomplish the methods, we will use the Care Facilities’ common area video camera system to corroborate sensor fall activations are actual falls. Second, we will use the same passive system technology to explore gait measurement as an additional indicator of an oncoming health changes such as a fall. We will use gait parameter measuring technology in a Care Facility medical office for regular vital monitoring. We will use gait measurements with Facility fall reports to explore the effectiveness of our predictive fall risk model against industry-standard fall risk assessments. Future directions will include ASSET launching Beta trials of the product among Care Facilities for final refinement of the product before full release to the public.Project Narrative Falls are the leading cause of injury-related death in adults above the age of 65, and Care Facilities (e.g. assisted living, nursing homes) bear a substantial portion of the cost due to liability. Yet, this simple motion can be avoided if non-injurious falls and changes in gait parameters, both of which are predictors of future injurious falls, are recognized in time for preventative healthcare measures to be enacted. ASSET, in partnership with the University of South Carolina, is leveraging floor vibrations to detect non-injurious falls and changes in gait parameters as an affordable solution that alerts Care Facilities before an injurious fall occurs.