An Edge-AI Pose-Estimation Platform for Real-Time Posture and Posture-Transition Monitoring: A Feasibility Study for Pre-Emptive Fall-Risk Alerting in Elderly and Dementia Care

Falls and unmonitored patient movements remain leading causes of harm in healthcare settings. Current solutions - such as weight-based alarms, remote video monitoring, and in-person sitters - are reactive, labor-intensive, or prone to false alarms, often compromising both care quality and staff efficiency. We developed a machine learning-enabled, proactive-preventive patient monitoring platform that uses 17-keypoint pose estimation to detect changes in patient posture and generate alerts in real time. The Edge-AI device integrates a Raspberry Pi 5, Hailo-8L neural processing unit (NPU), and Ra

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