Investigation of an Indoor Air Quality Sensor for Asthma Management in Children

被引：28

作者：

Jaimini U. ^{[1
]}

Banerjee T. ^{[1
]}

Romine W. ^{[2
]}

Thirunarayan K. ^{[1
]}

Sheth A. ^{[1
]}

Kalra M. ^{[3
]}

机构：

[1] Ohio Center of Excellence in Knowledge-Enabled Computing (Kno.e.sis), Wright State University, Dayton, 45435, OH

[2] Department of Biological Sciences, Wright State University, Dayton, 45435, OH

[3] Dayton Children's Hospital, Dayton, 45404, OH

来源：

IEEE Sensors Letters | 2017年 / 1卷 / 02期

基金：

美国国家卫生研究院;

关键词：

asthma management; cooking; indoor air quality sensor and smoking; Sensor applications;

D O I：

10.1109/LSENS.2017.2691677

中图分类号：

学科分类号：

摘要：

Monitoring indoor air quality is critical because Americans spend 93 of their life indoors, and around 6.3 million children suffer from asthma. We want to passively and unobtrusively monitor the asthma patient's environment to detect the presence of two asthma-exacerbating activities: smoking and cooking using the Foobot sensor. We propose a data-driven approach to develop a continuous monitoring-activity detection system aimed at understanding and improving indoor air quality in asthma management. In this study, we were successfully able to detect a high concentration of particulate matter, volatile organic compounds, and carbon dioxide during cooking and smoking activities. We detected 1) smoking with an error rate of 1; 2) cooking with an error rate of 11; and 3) obtained an overall 95.7 percent accuracy classification across all events (control, cooking and smoking). Such a system will allow doctors and clinicians to correlate potential asthma symptoms and exacerbation reports from patients with environmental factors without having to personally be present. © 2017 IEEE.

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