|Title||Investigation of an Indoor Air Quality Sensor for Asthma Management in Children|
|Publication Type||Journal Article|
|Year of Publication||2017|
|Authors||Utkarshani Jaimini, Tanvi Banerjee, William Romine, Krishnaprasad Thirunarayan, Amit Sheth, Maninder Kalra|
|Journal||IEEE Sensors Letters|
Abstract—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.
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