Title: Automatic Sensing System to Detect Falls and Fall-Risk in Elders. Project Summary and Abstract One in every three people age 65 or older falls each year, making falls the most common cause of injuries and hospitalizations for trauma in older adults and the leading cause of death due to injury (CDC, 2006). Researchers have studied falls, fall risk assessment, and interventions to prevent falls. However, to date, their methods require that research staff or clinicians complete multi-factorial assessments of fall risk and that research subjects maintain logs of falls, wear devices that measure changes in positions that could indicate a fall or activate an alarm that they need assistance. Building on our current work, we propose to validate and deploy an innovative technological approach that automatically detects when falls have occurred or when the risk of falls is increasing. Subjects will not have to press buttons, pull cords or wear any devices. This new """"""""passive"""""""" approach using sensors in the home could revolutionize detecting and preventing falls as well as measuring fall risk. By detecting falls or increasing fall risk early, this new technology can act as a trigger for elders, family members, or health care providers to improve physical function or better manage illnesses that are precipitating falls. The products of this study can improve access to fall risk measures by deploying the new sensor system in any private house or apartment as well as senior centers, churches, or retail stores. In such settings, persons could go to an accessible area to perform the guided motions to be measured by the sensor network developed in this application. In just a few minutes, a person would have a reliable fall risk indicator to alert increasing fall risk. An automatic sensing system to detect falls has major potential in senior housing, long-term care settings, private community housing as well in acute care settings where falls are a major risk. After laboratory validation using 567 falls performed by trained theater stunt actors, we will deploy the prototype sensing system for two years of data collection in ten apartments of elders in an independent living setting to complete validation and field testing (again using 960 falls performed by stunt actors). This application integrates the specialized talents and perspectives of not only health care scientific disciplines (nursing, physical therapy, social work, medicine) but also electrical and computer engineering, computer science, and informatics. This application will be of interest to AHRQ and likely the Innovations and Emerging Areas Portfolio that """"""""will foster and nurture ideas and projects that have potential to lead to highly innovative solutions that may lead to significant advances in healthcare practice...""""""""
(3 sentence) relevance of research to public health Falls are the most common cause of injuries and hospitalizations for trauma in older adults and the leading cause of death due to injury (CDC, 2006). We propose to validate an innovative technological approach that automatically detects when falls have occurred or when the risk of falls is increasing. The products of this study will improve access to fall risk assessment and fall detection by deploying the new sensor system in any private house or apartment as well as senior centers, churches, or retail stores.
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|Su, Bo Yu; Ho, K C; Rantz, Marilyn J et al. (2015) Doppler radar fall activity detection using the wavelet transform. IEEE Trans Biomed Eng 62:865-75|
|Stone, Erik E; Skubic, Marjorie (2015) Fall detection in homes of older adults using the Microsoft Kinect. IEEE J Biomed Health Inform 19:290-301|
|Rantz, Marilyn J; Skubic, Marjorie; Popescu, Mihail et al. (2015) A New Paradigm of Technology-Enabled â€˜Vital Signsâ€™ for Early Detection of Health Change for Older Adults. Gerontology 61:281-90|
|Stone, Erik E; Skubic, Marjorie; Back, Jessica (2014) Automated health alerts from Kinect-based in-home gait measurements. Conf Proc IEEE Eng Med Biol Soc 2014:2961-4|
|Banerjee, Tanvi; Enayati, Moein; Keller, James M et al. (2014) Monitoring patients in hospital beds using unobtrusive depth sensors. Conf Proc IEEE Eng Med Biol Soc 2014:5904-7|
|Liu, Liang; Popescu, Mihail; Skubic, Marjorie et al. (2014) An automatic fall detection framework using data fusion of Doppler radar and motion sensor network. Conf Proc IEEE Eng Med Biol Soc 2014:5940-3|
|Enayati, Moein; Banerjee, Tanvi; Popescu, Mihail et al. (2014) A novel web-based depth video rewind approach toward fall preventive interventions in hospitals. Conf Proc IEEE Eng Med Biol Soc 2014:4511-4|
|Wang, Fang; Skubic, Marjorie; Rantz, Marilyn et al. (2014) Quantitative gait measurement with pulse-Doppler radar for passive in-home gait assessment. IEEE Trans Biomed Eng 61:2434-43|
|Stone, Erik E; Skubic, Marjorie (2013) Unobtrusive, continuous, in-home gait measurement using the Microsoft Kinect. IEEE Trans Biomed Eng 60:2925-32|
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