Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Development and Validation of a Digital Image Processing-Based Pill Detection Tool for an Oral Medication Self-Monitoring System

Version 1 : Received: 11 February 2022 / Approved: 17 February 2022 / Online: 17 February 2022 (08:45:14 CET)

A peer-reviewed article of this Preprint also exists.

Holtkötter, J.; Amaral, R.; Almeida, R.; Jácome, C.; Cardoso, R.; Pereira, A.; Pereira, M.; Chon, K.H.; Fonseca, J.A. Development and Validation of a Digital Image Processing-Based Pill Detection Tool for an Oral Medication Self-Monitoring System. Sensors 2022, 22, 2958. Holtkötter, J.; Amaral, R.; Almeida, R.; Jácome, C.; Cardoso, R.; Pereira, A.; Pereira, M.; Chon, K.H.; Fonseca, J.A. Development and Validation of a Digital Image Processing-Based Pill Detection Tool for an Oral Medication Self-Monitoring System. Sensors 2022, 22, 2958.

Abstract

Objective tools to track medication adherence are lacking. A tool to monitor pill intake that can be implemented in mHealth apps 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 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 need to be further improved, in particular for the detection of pills with non-oval shapes.

Keywords

computer vision; image processing; medication adherence; object detection; pill detection

Subject

Medicine and Pharmacology, Pharmacy

Comments (0)

We encourage comments and feedback from a broad range of readers. See criteria for comments and our Diversity statement.

Leave a public comment
Send a private comment to the author(s)
* All users must log in before leaving a comment
Views 0
Downloads 0
Comments 0
Metrics 0


×
Alerts
Notify me about updates to this article or when a peer-reviewed version is published.
We use cookies on our website to ensure you get the best experience.
Read more about our cookies here.