Noninvasive Patient Monitoring with Ambient Sensors to Monitor Physical and Cognitive Health for Individuals Living with Alzheimer’s Disease
Brian Bradley Johnson · 2024
Abstract An estimated 6.2 million Americans aged 65 or older live with Alzheimer’s Disease and Alzheimer’s Disease Related Dementias in the United States, and 55 million globally. Fall detection, prediction, and prevention patient monitoring technology for this population has not been widely adopted as the standard of care because of privacy concerns with artificial intelligent video surveillance and problematic user experience design with wearables. The current standard of care for falls is eyewitness or self-report and therefore highly susceptible to human error. Therefore, solutions need to be scalable, affordable, and clinically effective with broad technology user acceptance. Despite many prevention and intervention methods that have been tried in past decades, falls remain the number one concern in aging care. The Centers for Medicare and Medicaid Services pay an enormous cost estimated at $50B per year on falls and fall related injuries. Human activity recognition with noninvasive ambient sensors is a versatile approach to patient monitoring as human movement can be translated into activities of daily living with sophisticated algorithms. This method does not use cameras and requires no contact with the body and thus it could be accepted at a higher rate than previous technologies and could therefore be adopted as the standard of care across the continuum.