Falls are the most common cause of elderly individuals being forced to transition from independent living to assisted care. With this transition often comes a decrease in quality of life and always comes a tremendous increase in healthcare costs, which are not sustainable given the forecasted rise in the number of elderly people over the coming decades. Reducing fall rates is therefore an important goal for both individuals and the entire healthcare industry. Therapeutic techniques are being developed to help reduce fall risk, but their success depends both on accurately diagnosing fall prone individuals and on precisely assessing the benefits the techniques provide. Researchers are working towards providing the capabilities by studying the relationship between gait, posture, and falls. Much of this work is done in modern motion capture laboratories, which collect precise gait and posture data but are expensive and immobile, limiting their capabilities for continuous, long-term data collection in natural settings. As a result, gait researchers have had limited success achieving accurate fall risk assessment in diverse patient sets. The proposed project addresses these limitations by developing and validating a custom-designed wearable wireless sensor system that collects accurate and precise gait and posture data continuously and non-invasively in any location. The system will be built on top of the TEMPO (Technology Enabled Medical Precision Observation) system, an accelerometer-based wearable sensor system developed at the University of Virginia that is currently being used with success by medical researchers to monitor and assess tremor in Parkinson?s Disease and Essential Tremor patients. Preliminary results from a pilot study at Virginia Tech show great promise in such a system?s ability to provide high application fidelity, even if the raw motion data is less precise than a laboratory-based motion capture laboratories.
