Pose-Based Fall Detection System: Efficient Monitoring on Standard CPUs
This provides a practical, resource-efficient solution for enhancing safety in assisted living homes, though it is incremental as it builds on existing pose estimation techniques.
The paper tackles fall detection for elderly residents by developing a system that uses pose estimation and threshold-based analysis to distinguish falls from other activities, achieving high accuracy with real-time processing on standard CPUs without requiring additional sensors or GPUs.
Falls among elderly residents in assisted living homes pose significant health risks, often leading to injuries and a decreased quality of life. Current fall detection solutions typically rely on sensor-based systems that require dedicated hardware, or on video-based models that demand high computational resources and GPUs for real-time processing. In contrast, this paper presents a robust fall detection system that does not require any additional sensors or high-powered hardware. The system uses pose estimation techniques, combined with threshold-based analysis and a voting mechanism, to effectively distinguish between fall and non-fall activities. For pose detection, we leverage MediaPipe, a lightweight and efficient framework that enables real-time processing on standard CPUs with minimal computational overhead. By analyzing motion, body position, and key pose points, the system processes pose features with a 20-frame buffer, minimizing false positives and maintaining high accuracy even in real-world settings. This unobtrusive, resource-efficient approach provides a practical solution for enhancing resident safety in old age homes, without the need for expensive sensors or high-end computational resources.