用摄像头像素变化追踪呼吸，无需接触传感器。
Pixel Intensity Tracking for Remote Respiratory Monitoring: A Study on Indonesian Subject
- 通过像素强度变化结合光流算法实现非接触式呼吸监测。
- 静态条件下最低误差为MAE 0.85，动态下达MAE 0.81。
- 适合无感健康监测、居家老人看护等场景使用。
呼吸率是反映多种健康状况的重要生命体征。传统接触式测量方法常令人不适，而呼吸带和智能手表等替代方案在成本与操作性上存在局限。为此，本文提出一种基于RGB相机图像像素强度变化（PIC）的非接触式呼吸监测方法。实验测试了3种边界框大小、3种滤波器（Laplacian、Sobel、无滤波）及2种角点检测算法（ShiTomasi、Harris），采用Lukas-Kanade光流法进行跟踪。共在67名受试者中测试了18种配置，涵盖静态与动态条件。静态条件下最优组合为中等边界框、Sobel滤波与Harris角点检测（MAE: 0.85，RMSE: 1.49）。动态条件下，大边界框+无滤波+ShiTomasi，或中等边界框+无滤波+Harris均取得最低误差（MAE: 0.81，RMSE: 1.35）。
原文摘要 · Abstract (English)
Respiratory rate is a vital sign indicating various health conditions. Traditional contact-based measurement methods are often uncomfortable, and alternatives like respiratory belts and smartwatches have limitations in cost and operability. Therefore, a non-contact method based on Pixel Intensity Changes (PIC) with RGB camera images is proposed. Experiments involved 3 sizes of bounding boxes, 3 filter options (Laplacian, Sobel, and no filter), and 2 corner detection algorithms (ShiTomasi and Harris), with tracking using the Lukas-Kanade algorithm. Eighteen configurations were tested on 67 subjects in static and dynamic conditions. The best results in static conditions were achieved with the Medium Bounding box, Sobel Filter, and Harris Method (MAE: 0.85, RMSE: 1.49). In dynamic conditions, the Large Bounding box with no filter and ShiTomasi, and Medium Bounding box with no filter and Harris, produced the lowest MAE (0.81) and RMSE (1.35)
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