Continuous Fall Risk Monitoring System: Walking vs Activities of Daily Living
Small Business Information
AFRAME DIGITAL, INC.
40272 HIDDENHEIGHTS LN, LOVETTSVILLE, VA, -
Name: AMY PAPADOPOULOS
Phone: (703) 560-0513
Phone: (703) 560-0513
Name: CINDY CRUMP
Phone: (571) 308-0152
Phone: (571) 308-0152
AbstractDESCRIPTION (provided by applicant): Falls are the leading cause of injury-related visits to U.S. emergency departments and the primary etiology of accidental deaths in persons over the age of 65 years1. Interventions such as physical therapy, adjusting medications, or behavior changes can reduce the elderly fall rate2. Fall risk determination is needed to identify who may benefit from interventions. Changes in fall risk may occur suddenly or gradually, and are more likely to become apparent in the home environment as an individual goes about their normal activities of daily living (ADLs) rather than during a limited and periodic care provider assessment. Consequently, a continuous, all-in-one system that would monitor elderly individuals in the home for signs they are becoming more susceptible to falls is needed to reduce falls in the elderly. To be effective, the monitoring device must be non-intrusive and socially acceptable. The long-term goal of this project is to [extend an existing non-intrusive, commercially available monitoring system capable of location tracking, physiologic monitoring, and alerting to also assess fall risk in real time; the system would consider factors such as frequent bathroom use, fitful sleep and changes in gait characteristics]. The monitor for the proposed research has a watch form factor, is worn at the wrist, and has demonstrated high acceptance rates by elderly users. Research has shown that abnormal gait is indicative of fall risk, leading to the use of a variety of measurements of gait in fall risk determinations. To analyze gait from data gathered during ADLs, it is necessary to differentiate periods of walking, which can then be analyzed for abnormal characteristics. The purpose of this Phase I proposal is to test the [feasibility of using tri-axial acceleration data gathered from a commercially available wrist monitor to recognize periods of walking. Walking data can then be used in conjunction with other system data to] make inferences about changes in fall risk. Thirtyelderly (aged 65 and over), ambulatory volunteers residing in an independent living facility will be recruited. The volunteers will be asked to engage in normal ADLs while being monitored over a 4-hour time period. During the study, volunteers will be videotaped, monitored using the wrist device, and monitored using a body-area sensor network technology developed at the University of Virginia. The specific aims of this project will be to: 1) determine if it is feasible to distinguish between periods of walking and other ADLs using the presence of frequencies generally associated with walking in wrist- gathered acceleration data as the differentiator, 2) determine whether individuals typically demonstrate a narrower range of walking frequencies than that suggested for the entire population, and 3) determine if it is feasible to use machine learning and time-series techniques to distinguish between periods of walking and other ADLs using characteristics learned from walking and non-walking data. PUBLIC HEALTH RELEVANCE: Falls are the leading cause of injury-related visits to emergency departments in the United States and the primary cause of accidental deaths in persons over age 651. In order to reduce the number of falls, a continuous, non-intrusive, convenient monitoring system in an acceptable form factor which will monitor elderly individuals in their home environment for signs they are becoming more susceptible to falls needs to be developed. The long-term goal of this project is to develop such a system; the proposed project would take the necessary step of identifying and differentiating between walking and other normal activities [so that data gathered during walking can be used to recognize changes in stability for an individual, and then can be used in conjunction with other system data already being automatically collected in real-time to recognize an increased probability of falling both during walking or other activities.]
* information listed above is at the time of submission.