Enhanced Fall Prevention in Nursing Facilities: Assisting Caregivers through Data-Driven Selective Monitoring and Notification

Abstract

Falls in nursing homes predominantly occur when elderly residents are unwatched, a situation exacerbated by critical workforce shortages in Japan where 69.3% of facilities report caregiver deficits. Our research develops a selective monitoring system that strategically targets high-risk residents through three phases: targeting (identifying risk using body temperature data), monitoring (detecting risky activities in fall-prone locations), and intervening (providing multi-channel feedback). We took an approach to predict the potential occurrence of fall accidents, as well as caregivers' intuitive "sense of risk", achieving practical results despite the former proving challenging due to data imbalance. We believe the prediction of ‘sense of risk’ could serve as a valuable proxy that translates caregivers' tacit knowledge into actionable monitoring protocols in resource-constrained environments. The system delivers notifications through alarm lamps, audio instructions, and smartphone alerts to facilitate timely intervention. Future work will focus on enriching understanding of caregivers' risk assessment, implementing near-miss reporting, and expanded usability testing. This selective approach demonstrates technology's potential to augment human caregiving by focusing on resources where most needed in aging societies.

Keywords: Fall Prevention, Nursing Facilities, Selective Monitoring, Caregiver Assistance, Healthcare Automation, Risk Prediction Algorithms

DOI: 10.54941/ahfe1006187