Every year, about a third of adults aged 65 years or over fall at least once, and for many of these individuals' falls result in mild to severe injuries. For this reason, identifying older adults who are at risk of falling before their first fall is an important healthcare priority. Many screening tools exist for the identification of adults at risk of falling; however, these tools often rely on paper and pencil questionnaires, or the performance of simplistic tasks that do not represent the breadth and complexity of common daily activities that lead to falls. These fall assessment tools are inadequate, because they do not realistically represent environmental factors at play during activities of daily living (ADLs), especially those that influence walking and balance. While it is not feasible to recreate the vast assortment of "real-life" ADL scenarios in clinical or residential settings, immersive Virtual Reality (VR) is a tool that can produce myriad environmental constraints, cognitive demands, and assessment scenarios that are rich, customized and easily varied, while only taking up a small footprint. This project will capitalize on recent technological advances in immersive VR to develop an objective fall risk assessment tool that reliably quantifies performance in a variety of real-world scenarios. The long-term outcomes of this multi-disciplinary research will have a broad and transformative impact on how fall risk is identified, will lead to more successful and targeted fall prevention programs, and will significantly reduce the healthcare costs associated with falls.

The overall goal of this research is to create normed and validated VR fall-risk assessment scenarios capable of simulating everyday activities. To generate these scenarios, the project will first determine how whole-body movement patterns within VR and natural environments are influenced by the visual information available to users of the system. This knowledge will then be used to create ecologically valid fall assessment scenarios that will immersively simulate one environment where older adults are known to be at high risk of falls. Finally, the scenarios will systematically be validated both on healthy older adults and on adults at risk of falling, to determine how to most effectively and efficiently characterize fall risk.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

National Science Foundation (NSF)
Division of Information and Intelligent Systems (IIS)
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Ephraim Glinert
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University of Wisconsin Madison
United States
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