Increasing popularity of inhaled therapy stimulates research on smart devices. A major concern is the variability of the drug dose delivered to the lungs from the inhalation devices due to differences in the patients' inhalation pro les. In this project we are interested in using microphones embedded in modern smartphones to accurately monitor the patient's inhalation manouvre.
The major problem here is how to avoid extensive microphone calibration and yet provide accurate measurements of the air flow. In this thesis, we would like to use deep learning methods to achieve the goals and implement a smartphone app as a proof of concept. You will need to gather data with several smartphones of different types and experiment with various inhalers. Your goal is to make accurate inhalation efficiency measurements possible with zero effort. We have some ideas how to achieve this, but you will get a lot of experimentation freedom!
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