The rising cost of long-term patient care, the shortage of nurses, and the increasing number of seniors in the United States make it imperative to investigate the possibility of intelligent systems for elder/patient care. One challenge is how to prevent falls, which often result in serious injury and considerable hospital and patient costs. In this exploratory project the PI and her team will focus on analyzing sensory data of patient actions in an effort to develop algorithms for the automatic detection and prediction of falls among elderly patients. Their goal is to gain a good understanding of how multimodal sensory data combined with domain knowledge of falls can be used to characterize pre-fall patient actions, in order to determine the feasibility of developing automatic alert systems that incorporate machine learning algorithms to assist human nurses and robotic caregivers by warning of potential falls.
Broader Impacts: Project outcomes will pave the way for future development of intelligent systems to reduce the incidence of patient falls, which is a major societal concern. The project will provide a rich spectrum of interdisciplinary training for graduate student researchers, and will also strengthen UNC Charlotte's existing programs in broadening participation in computing and in research experiences for undergraduates (REU) by deepening involvement of women and minority undergraduate students in research.
