Asthma is a widespread chronic pulmonary disease. Poor management of Asthma results in over 450,000 hospitalizations in the US each year, the majority of which are preventable by strict adherence to asthma control medication. In this thesis, we propose, design and benchmark SmartInhaler, a system which performs automated and unobtrusive measurement of inhaler usage behavior and tracking of outdoor air quality parameters.
SmartInhaler consists of an attachment to Metered Dose Inhalers (MDI) developed as a configurable platform with an aim to measure patient adherence to asthma control medication. The attachment is designed to track the parameters associated with asthma medication dosage: number of dosages, time stamp of dosage, location of use and verify the patient taking the medication. Furthermore, SmartInhaler can also track outdoor air quality parameters (concentration of pollutants) using readily available online pollution data based on the location of use. Tracking air quality is
useful for the asthma patients since their lung airways can be sensitive to air pollutants.
The SmartInhaler attachment communicates with smart phones or tablets through a custom Android API developed for delay-tolerant data collection for both adherence and air quality. In this thesis, we also demonstrate a proof of concept training module for correcting MDI dosage administration technique through air-flow modeling.