用深度相机和边缘设备实现隐私保护的跌倒实时检测
Stereo Vision-Based Fall Prediction and Detection using Human Pose Estimation on the AMD Kria K26 SOM
- 通过深度图像估测人体关键点，不保留原始画面
- 在边缘设备上实现75.85%的跌倒识别准确率
- 适合老年人居家监测与医疗辅助场景
老年人跌倒可能造成严重伤害并降低生活质量。本研究提出一种便携、低功耗、电池供电的视觉跌倒预测与检测系统，基于AMD Kria K26系统级模块（SOM）上的人体姿态估计（HPE）。系统通过USB连接Intel RealSense D455深度相机，以60 FPS同步捕获640×480×3的彩色帧与640×480的深度帧。采用三阶段流水线：量化版YOLOX从彩色帧中定位人体边界框，随后丢弃彩色帧以保护隐私；A2J模型利用深度帧估计每人15个关节关键点；最后由卷积神经网络（CNN）基于关键点坐标（x, y, z）进行跌倒分类。模型分别在CrowdHuman、ITOP、MP-3DHP、UR跌倒检测及自建SDSU PSG数据集上训练。单核DPU串行运行时吞吐量为2.5 FPS，双核多线程下提升至4.5 FPS。评估结果显示：YOLOX的交并比（IoU≥50%）准确率为74%，A2J的mAP（10cm规则）为84.13%，CNN分类准确率为75.85%。结果证明该方案可在边缘设备上实现无云依赖的隐私保护式跌倒检测，适用于老年监护与智能医疗。
原文摘要 · Abstract (English)
Background and Objective: Falls among elderly people can cause serious injury and reduce quality of life. Timely prediction and detection are essential to prevent harm and support well-being. We propose a portable, low-power, battery-operated, vision-based fall prediction and detection system using HPE on an AMD Kria K26 System-on-Module (SOM). The objective is a non-intrusive, privacy-preserving system for real-time fall detection. Methods: The system uses an Intel RealSense D455 range-sensing camera connected to the K26 SOM by USB. It captures synchronized RGB and depth frames, 640 x 480 x 3 and 640 x 480 pixels, at 60 FPS. The SOM runs a three-stage pipeline with quantized YOLOX, Anchor-to-Joint (A2J), and fall-detection models. YOLOX identifies human bounding boxes from RGB frames, then discards the RGB frames to preserve privacy. A2J uses depth frames to estimate 15 joint keypoints per person. A CNN uses selected joint coordinates (x, y, z) to classify fall activity. YOLOX was trained on CrowdHuman; A2J on ITOP, MP-3DHP, UR Fall Detection, and a custom SDSU PSG dataset; and the CNN on UR Fall Detection and SDSU PSG. The design used a single-core DPU with a serial pipeline and a dual-core DPU running YOLOX and A2J with multiple threads. Results: Quantized accuracy was evaluated using IoU >= 50% for YOLOX, mAP with a 10-cm rule for A2J, and classification accuracy, (TP + TN)/(TP + TN + FP + FN), for the CNN. Accuracies were 74%, 84.13%, and 75.85%. Throughput improved from 2.5 FPS for the single-threaded pipeline to 4.5 FPS for the multi-threaded version. Conclusion: Results demonstrate the feasibility of privacy-preserving fall detection on an AMD Kria K26 edge device. On-device HPE and fall classification runs without cloud dependency, supporting elderly monitoring and assistive healthcare. Future work will improve model accuracy and speed.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。