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Development and validation of a digital image processing-based pill detection tool for an oral medication self-monitoring system

2022-04-12Journal article Open access
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dc.contributor.authorHoltkötter, Jannis
dc.contributor.authorAmaral, Rita
dc.contributor.authorAlmeida, Rute
dc.contributor.authorJácome, Cristina
dc.contributor.authorCardoso, Ricardo
dc.contributor.authorPereira, Ana
dc.contributor.authorPereira, Mariana
dc.contributor.authorChon, Ki H.
dc.contributor.authorFonseca, João Almeida
dc.date.accessioned2024-04-08T14:10:39Z
dc.date.available2024-04-08T14:10:39Z
dc.date.issued2022-04-12
dc.description.abstractLong-term adherence to medication is of critical importance for the successful management of chronic diseases. Objective tools to track oral medication adherence are either lacking, expensive, difficult to access, or require additional equipment. To improve medication adherence, cheap and easily accessible objective tools able to track compliance levels are necessary. A tool to monitor pill intake that can be implemented in mobile health solutions without the need for additional devices was developed. We propose a pill intake detection tool that uses digital image processing to analyze images of a blister to detect the presence of pills. The tool uses the Circular Hough Transform as a feature extraction technique and is therefore primarily useful for the detection of pills with a round shape. This pill detection tool is composed of two steps. First, the registration of a full blister and storing of reference values in a local database. Second, the detection and classification of taken and remaining pills in similar blisters, to determine the actual number of untaken pills. In the registration of round pills in full blisters, 100% of pills in gray blisters or blisters with a transparent cover were successfully detected. In the counting of untaken pills in partially opened blisters, 95.2% of remaining and 95.1% of taken pills were detected in gray blisters, while 88.2% of remaining and 80.8% of taken pills were detected in blisters with a transparent cover. The proposed tool provides promising results for the detection of round pills. However, the classification of taken and remaining pills needs to be further improved, in particular for the detection of pills with non-oval shapes.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationHoltkötter, J., Amaral, R., Almeida, R., Jácome, C., Cardoso, R., Pereira, A., Pereira, M., Chon, K. H., & Fonseca, J. A. (2022). Development and validation of a digital image processing-based pill detection tool for an oral medication self-monitoring system. Sensors, 22(8), Artigo 8. https://doi.org/10.3390/s22082958pt_PT
dc.identifier.doi10.3390/s22082958pt_PT
dc.identifier.eissn1424-8220
dc.identifier.urihttp://hdl.handle.net/10400.22/25307
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherMDPIpt_PT
dc.relationThis work was supported by POCI-01-0145-36 FEDER-029130 (“mINSPIRE—mHealth to measure and improve adherence to medication in chronic obstructive respiratory diseases—generalization and evaluation of gamification, peer support, and advanced image processing technologies”) through the European Regional Development Fund (ERDF), co-funded by the COMPETE2020 (Programa Operacional Competitividade e Internacionalização), Portugal 2020 and by Portuguese Funds through Fundação para a Ciência e a Tecnologia and by project NORTE-01-0247-FEDER-033275 (AIRDOC: Smart Mobile Application for Individualized Support and Monitoring of the Respiratory Function and Sounds of Chronic Obstructive Patients) financed by the North Portugal Regional Operational Program (NORTE 2020), under the PORTUGAL 2020 Partnership Agreement, and through the European Regional Development Fund (ERDF). AIRDOC is with ITEA 3 16040 PHE—Personal Health Empowerment project consortiumpt_PT
dc.relation.publisherversionhttps://www.mdpi.com/1424-8220/22/8/2958pt_PT
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/pt_PT
dc.subjectComputer visionpt_PT
dc.subjectImage processingpt_PT
dc.subjectMedication adherencept_PT
dc.subjectObject detectionpt_PT
dc.subjectPill detectionpt_PT
dc.titleDevelopment and validation of a digital image processing-based pill detection tool for an oral medication self-monitoring systempt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.citation.endPage16pt_PT
oaire.citation.startPage1pt_PT
oaire.citation.titleSensorspt_PT
oaire.citation.volume22(8)pt_PT
person.familyNameAmaral
person.givenNameRita
person.identifierR-00H-83K
person.identifier.ciencia-id1A1E-751F-50F0
person.identifier.orcid0000-0002-0233-830X
person.identifier.ridE-5535-2017
person.identifier.scopus-author-id56067841600
rcaap.rightsopenAccesspt_PT
rcaap.typearticlept_PT
relation.isAuthorOfPublication790fdd33-acdb-4dfe-88dc-38538486c9b3
relation.isAuthorOfPublication.latestForDiscovery790fdd33-acdb-4dfe-88dc-38538486c9b3

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