A Comparative Analysis of Pose Estimation Models and a Proposed MediaPipe-Based Framework for Vision-Based Fall Detection

Vision-based fall detection increasingly relies on human pose estimation to convert raw video into structured skeletal data for posture and motion analysis. This paper presents a literature-based comparative study of five widely used pose estimation models — MediaPipe Pose Landmarker, OpenPose, AlphaPose, MoveNet, and HRNet — examined across architecture, accuracy, speed, computational cost, edge-deployment readiness, multi-person capability, and healthcare suitability. The comparison surfaces a research gap: existing fall-detection literature rarely justifies its choice of pose-estimation backbone through systematic comparison, and almost no published study evaluates these backbones together under fall-specific, resource-constrained conditions. Motivated by this gap, and by published evidence favoring lightweight, CPU-feasible architectures for continuous healthcare monitoring, this paper proposes a conceptual prototype framework for fall detection built around MediaPipe Pose Landmarker, combining pose-based posture analysis with temporal inactivity reasoning to suppressfalsealarms.Theproposedframeworkispresentedstrictlyatthearchitecturalandworkflowlevel;noexperimentalimplementati on, datasetevaluation,orperformanceresultsarereportedhere.Thispaperestablishestheliterature-grounded foundation and prototype design that will be implemented and experimentally validated in a subsequent study.