(untitled) (International Journal of Engineering and Computer Science, 2018)
Nowadays, 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 optical dust sensor, humidity sensor and temperature 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. 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. The data from the sensor can be transmitted to the cloud if we need.