AI-based fall detection system may transform care for people with dementia
Dr. Marcia Ory, Regents and University Distinguished Professor in the Department of Environmental and Occupational Health at the Texas A&M University School of Public Health, has been awarded $1.3 million to collaborate on testing an automated fall detection and fall risk prediction system for persons living with dementia. The funding is part of a Small Business Innovative Research Grant obtained by her industry partner, Clairvoyant Networks, Inc.
The funding, from the National Institute on Aging, will be used to develop a system that simplifies and improves risk assessment for, and detection of, mobility-based falls, and quickly sends alerts after a person living with dementia falls.
The Clairvoyant Networks project will develop and test an automated fall detection system using artificial intelligence in a neural-network to understand the highly precise data from ultra- wideband (UWB) technology, a radio technology that collects data related to sensors, location and tracking and is used in smartphones and similar devices.
This project will move from lab studies to real-world applications in two phases. In phase 1, the team will test the ability of Clairvoyant’s fall detection system Theora® 360 to detect simulated falls in a laboratory setting.
Phase 2 will begin once 90 percent sensitivity to falls and 90 percent specificity in fall detection in the lab are reached and the protocols, AI/neural network, data platforms and other data are codified. In this phase, the team will assess Theora® 360’s ability to detect mobility-based falls and to predict changes in fall risk over time among people living with dementia in real-world community settings. For the latter, the team will develop an algorithm for activity modeling and risk profiling based on the overall mobility, gait and daily routines of 60 care recipients who live at home with a caregiver.