Author(s):
Vieira-Marques, Pedro ; Teixeira, João Fonseca ; Valente, José ; Pinho, Bernardo ; Guedes, Rui ; Almeida, Rute ; Jácome, Cristina ; Pereira, Ana ; Jacinto, Tiago ; Amaral, Rita ; Gonçalves, Ivânia ; Sousa, Ana Sá ; Couto, Mariana ; Magalhães, Manuel ; Bordalo, Diana ; Silva, Luís Nogueira ; Fonseca, J. Almeida
Date: 2020
Persistent ID: http://hdl.handle.net/10400.22/16658
Origin: Repositório Científico do Instituto Politécnico do Porto
Subject(s): Medication adherence; Smart device; Image
Description
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.
