Aframe Receives Funds for Advanced Health Monitoring System Research

Aframe Digital has revealed that the National Institute of Health and the National Institute on Aging have granted a follow-on grant fund for conducting research in the reduction of falls of the elderly by using its mobile care monitor platform.

The mobile care device is in the form of a wrist watch which provides real time data such as the movement and location of the elderly through cloud-based alerting and monitoring systems. In the United States the main cause for injury in the elderly is due to falls, which sometimes turn fatal for those above the age of 65. Using the mobile monitoring technology from Aframe, subtle changes in the gait could be detected immediately and communicated to the medical personnel and caregivers prior to an actual fall.

Dr. John Lach, Associate Professor in the Charles L. Brown Department of Electrical and Computer Engineering (ECE) at the University of Virginia will be assisting Aframe in the research. Dr. Amy Papadopoulos, senior research scientist at Aframe, explained that for monitoring one’s gait continuously it is important to know the normal routine day-to-day activities. After gaining this knowledge the walking period can be isolated from the other activities and it is possible to further study the abnormalities in the gait. The study will be conducted at Vinson Hall where there are 30 independent living resident volunteers who are 65 years and above. The CEO of Vinson Hall Retirement Community stated that they strongly supported research for fall reduction and that the Aframe research was significant in bringing the fall rate down. Papadopoulos explained that Vinson Hall was the ideal environment to conduct the research and that the follow on research will definitely produce useful results with the help of expertise form the Electrical and Computer engineering at the Virginia University on body sensor networks using which additional data could be gathered from the chest and legs. Papadopoulos concluded that this research aimed at isolating walking periods from the period of other activities by using machine learning techniques. Once this isolation is accomplished the individual’s gait will be analysed and methods will be devised to prevent falls.