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Conference Proceeding

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Falls are among the most serious accidents among the elderly leading to increased injuries, reduced functioning and mortality. In 2009, about 2.2 million nonfatal fall injuries were reported among the elderly population (CDC, 2010). In this study, eleven community dwelling elderly (aged 65-84 years) participated in fall risk assessment camp at sterling senior center organized by Northern Virginia Fall Prevention Coalition (NVFPC). Three custom made wireless inertial measurement units (IMUs) were attached on trunk and both shanks. All participants performed postural and locomotor tasks such as sit-to-stand (STS) and timed up and go (TUG). Temporal and kinematic parameters were obtained. Raw signals obtained were denoised using ensemble empirical mode decomposition and savistzky-golay filtering. The mean and standard deviation of TUG time and STS completion time for participants were found to be 11.3±6.6 sec and 3.58±2.07 sec respectively. The high variation in the result may be due to the use of assistive devices (i.e., cane and walker) by two participants. The objective of this study is to classify fall prone community dwelling individuals using non-invasive system. Four participants were classified as fall prone, three without fall risk and four were at potential risk based on their objective assessment and task performance. This system provides a platform for identifying fall prone individuals and may be used for early fall interventions among the elderly.


This is a pre-copy-editing, author-produced PDF of a conference article accepted for publication in Biomedical Sciences Instrumentation, volume 48, in 2012.


Copyright © (2012) International Society of Automation. All rights reserved. Used with permission of the International Society of Automation.



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