Edge AI System Predicts and Detects Falls Using Pose Estimation

Researchers have developed a vision-based system for predicting and detecting falls in elderly individuals, utilizing human pose estimation on an AMD Kria K26 System-on-Module (SOM). The system captures RGB and depth data, processes it on the edge device to estimate joint keypoints, and then classifies fall activity using a CNN, all while discarding RGB frames to preserve privacy. The multi-threaded pipeline achieved a throughput of 4.5 FPS and demonstrated the feasibility of a cloud-independent, privacy-preserving solution for elderly monitoring. AI

IMPACT Enables privacy-preserving, real-time fall detection for elderly care on low-power edge devices.

RANK_REASON The cluster describes a research paper detailing a novel system for fall detection using AI on edge hardware. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CV →

• AMD Kria K26 SOM
• Anchor-to-Joint (A2J)
• Christopher Paolini
• CNN
• CrowdHuman
• Intel RealSense D455
• MP-3DHP
• SDSU PSG
• UR Fall Detection

• product
• other

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