Pose-Based Fall Detection System: Efficient Monitoring on Standard CPUs

• URL: http://arxiv.org/abs/2503.19501v1
• Date: Tue, 25 Mar 2025 09:49:36 GMT
• Title: Pose-Based Fall Detection System: Efficient Monitoring on Standard CPUs
• Abstract summary: This paper presents a robust fall detection system that does not require any additional sensors or high-powered hardware.<n>The system uses pose estimation techniques, combined with threshold-based analysis and a voting mechanism, to effectively distinguish between fall and non-fall activities.
• Score: 0.18416014644193066
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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