| Title: | Investigation of an Indoor Air Quality Sensor for Asthma Management in Children |
| Author(s): | Jaimini U; Banerjee T; Romine W; Thirunarayan K; Sheth A; Kalra M; |
| Address: | "Ohio Center of Excellence in Knowledge-Enabled Computing (Kno.e.sis), Wright State University, Dayton, OH 45435 USA. Department of Biological Sciences, Wright State University, Dayton, OH 45435 USA. Dayton Children's Hospital, Dayton, OH 45404 USA" |
| DOI: | 10.1109/LSENS.2017.2691677 |
| ISSN/ISBN: | 2475-1472 (Print) 2475-1472 (Electronic) |
| 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" |
| Keywords: | Sensor applications asthma management cooking indoor air quality sensor and smoking; |
| Notes: | "PubMed-not-MEDLINEJaimini, Utkarshani Banerjee, Tanvi Romine, William Thirunarayan, Krishnaprasad Sheth, Amit Kalra, Maninder eng R01 HD087132/HD/NICHD NIH HHS/ R01 MH105384/MH/NIMH NIH HHS/ 2017/10/31 IEEE Sens Lett. 2017 Apr; 1(2):6000204. doi: 10.1109/LSENS.2017.2691677. Epub 2017 Apr 6" |
