用普通电脑实时检测老人跌倒，无需额外设备。
Pose-Based Fall Detection System: Efficient Monitoring on Standard CPUs
- 基于姿态估计与投票机制判断跌倒行为
- 20帧缓冲处理，降低误报率，准确率高
- 可在普通CPU上运行，适合养老院部署
养老机构中老年人跌倒带来严重健康风险，常导致伤害和生活质量下降。现有跌倒检测系统多依赖专用传感器或需高性能硬件与GPU支持的视频模型。本文提出一种无需额外传感器或高端算力的鲁棒跌倒检测系统。该系统结合姿态估计与阈值分析、投票机制，通过分析运动特征、身体姿态及关键点位置，利用20帧缓冲处理姿态特征，在真实场景中有效区分跌倒与非跌倒动作。采用轻量级框架MediaPipe，实现标准CPU上的实时处理，计算开销极小。该无感、低资源方案为养老机构提供了实用的安全监测解决方案。
原文摘要 · Abstract (English)
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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