The adherence to inhaled controller medications is of critical importance to achieve good clinical results in patients with chronic respiratory diseases. To objectively verify the adherence, a detection tool was previously developed and integrated in the mobile application InspirerMundi, based on image processing methods. In this work, a new approach for enhanced adherence verification was developed. In a first phase template matching is employed to confirm the inhaler positioning and to locate the dose counter. In a second phase Google ML Kit framework is used for the detection of each numerical dose in the dose counter. The proposed approach was validated through a new detection tool pilot implementation, using a set of images collected by patients using the application in their daily life. Performance of each of the two phases was evaluated for a set of commonly used inhaler devices. Promising results were achieved showing the potential of mobile embedded sensors without the need for external devices.
CITATION STYLE
Vieira-Marques, P., Teixeira, J. F., Valente, J., Pinho, B., Guedes, R., Almeida, R., … Almeida Fonseca, J. (2020). Combined Image-Based Approach for Monitoring the Adherence to Inhaled Medications. In IFMBE Proceedings (Vol. 76, pp. 1399–1404). Springer. https://doi.org/10.1007/978-3-030-31635-8_171
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