Goal: The SARS-CoV-2 viral infection could cause severe acute respiratory syndrome, disturbing the regular breathing and leading to continuous coughing. Automatic respiration monitoring systems could provide the necessary metrics and warnings for timely intervention, especially for those with mild symptoms. Current respiration detection systems are expensive and too obtrusive for any large-scale deployment. Thus, a low-cost pervasive ambient sensor is proposed. Methods: We will posit a barometer on the working desk and develop a novel signal processing algorithm with a sparsity-based filter to remove the similar-frequency noise. Three modes (coughing, breathing and others) will be conducted to detect coughing and estimate different respiration rates. Results: The proposed system achieved 97.33% accuracy of cough detection and 98.98% specificity of respiration rate estimation. Conclusions: This system could be used as an effective screening tool for detecting subjects suffering from COVID-19 symptoms and enable large scale monitoring of patients diagnosed with or recovering.

中文翻译：

---

用于 COVID-19 大流行的普遍呼吸监测传感器

目标：SARS-CoV-2病毒感染可能导致严重的急性呼吸系统综合症，扰乱正常呼吸并导致持续咳嗽。自动呼吸监测系统可以为及时干预提供必要的指标和警告，特别是对于那些症状较轻的人。当前的呼吸检测系统价格昂贵，而且对于任何大规模部署来说都过于突兀。因此，提出了一种低成本的普遍环境传感器。方法：我们将在办公桌上放置一个气压计，并使用基于稀疏性的滤波器开发一种新颖的信号处理算法，以去除相似频率的噪声。将进行三种模式（咳嗽、呼吸等）来检测咳嗽并估计不同的呼吸频率。结果： 所提出的系统实现了 97.33% 的咳嗽检测准确率和 98.98% 的呼吸率估计特异性。 结论： 该系统可用作检测患有 COVID-19 症状的受试者的有效筛查工具，并能够对诊断出或康复的患者进行大规模监测。