HelpmePills: A Mobile Pill Recognition Tool for Elderly Persons

Cunha, António; Adão, Telmo; Trigueiros, Paula

doi:10.1016/j.protcy.2014.10.174

Cited by 29 publications

(17 citation statements)

References 9 publications

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“…The result illustrates how smartphones can be used to rapidly identify pills in-situ. A similar approach (i.e., utilising the same set of features) for identification of pharmaceutical is HelpmePills [10]. By relying on the smartphone's camera and computational power, the application aims to accommodate mobile pill recognition for elderly.…”

Section: Object Identificationmentioning

confidence: 99%

Assisted Medication Management in Elderly Care Using Miniaturised Near-Infrared Spectroscopy

Klakegg

Gonçalves

Luo

et al. 2018

Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.

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Near-infrared spectroscopy 1 (NIRS) measures the light reflected from objects to infer highly detailed information about their molecular composition. Traditionally, NIRS has been an instrument reserved for laboratory usage, but recently affordable and smaller devices for NIRS have proliferated. Pairing this technology with the ubiquitous smartphone opens up a plethora of new use cases. In this paper, we explore one such use case, namely medication management in a nursing home/elderly care centre. First, we conducted a qualitative user study with nurses working in an elderly care centre to examine the protocols and workflows involved in administering medication, and the nurses' perceptions on using this technology. Based on our findings, we identify the main impact areas that would benefit from introducing miniaturised NIRS. Finally, we demonstrate via a user study in a realistic scenario that miniaturised NIRS can be effectively used for medication management when leveraging appropriate machine learning techniques. Specifically, we assess the performance of multiple pre-processing and classification algorithms for a selected set of pharmaceuticals. In addition, we compare our solution with currently used methods for pharmaceutical identification in a local care centre. We hope that our reflection on the multiple aspects associated with the introduction of this device in an elderly care setting can help both academics and practitioners working on related problems.

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Section: Object Identificationmentioning

confidence: 99%

Assisted Medication Management in Elderly Care Using Miniaturised Near-Infrared Spectroscopy

Klakegg

Gonçalves

Luo

et al. 2018

Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.

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“…According to our study, software applications developed for facilitating pill identification could be categorized into three main categories based on their application platforms: (i) mobile application [3,4,7,8], (ii) desktop application [1, 2, Ornicha Choungaramvong, Supangkana Tongsuk, Surasak Srilamlert and Supphachai Thaicharoen are with Department of Computer Science, Faculty of Science, Srinakharinwirot University, 114 Sukhumvit 23, North Klongtoey Subdistrict, Wattana District, Bangkok 10110, Thailand. 11,13,16,18], and (iii) Web application [5,6,9,14,17].…”

Section: Introductionmentioning

confidence: 99%

“…For instances, in the mobile application platform, Cunha and his research team proposed a mobile application for pill identification based on shape, dimension, and color of the pills [4]. In their method, shape is determined using shape-matching function, dimension is calculated using the minimum bounding box of the pill, and color is identified using pixel-by-pixel comparisons.…”

Section: Introductionmentioning

confidence: 99%

iPill: An iOS Mobile Application for Automatic Pill Identification Based on Image Processing Technique and Eigenface Algorithm

2017

IJCCIE

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Abstract-Consumable medical drugs are currently available in a variety of forms such as liquid, capsule, and tablet. For the tablet/pill form, many of them are manufactured in very similar appearances, which could lead to incorrect medical consumption, particularly for elderly and visually-impaired people. For the past several years, a number of software applications have been developed by both academic and business sectors to facilitate pill identification. These tools are ranged from Web-based applications, desktop applications, and to mobile applications. The key approach used by most of these tools is using image processing techniques to extract pill characteristics from a pill's image, converting these features into some numerical forms such as a vector model, and comparing the features with those stored in the database using some similarity or distance measures to determine a matched pill. Rather than using direct pixel-based characteristics and vector model for pill comparisons, an investigation of bringing in a face recognition algorithm to assist pill identification is presented in this paper. The proposed method exploits a commonly-known face recognition algorithm, known as Eigenface algorithm, to mathematically derive features from pills' images. These acquired features are used for determining the pill's shape, which are then combined with pill's color as criteria for the overall process of pill identification. In addition, the proposed technique is implemented as an iOS mobile application, named iPill, for an automatic pill identification. A preliminary result using 320 pill images from 20 types of pills with 9 different shapes and 10 dissimilar colors shows that iPill mobile application gives a satisfactory performance on the accuracy for automatic pill identifications.

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“…In the academic literature the challenge of pill identification has been addressed by several groups [22], [23], [24], [25], [26]. All of these approaches share a common theme, the use of feature vectors designed based on domain knowledge with ranking based on the distances between the feature vectors of the reference images and consumer, query, images.…”

Section: Introductionmentioning

confidence: 99%

“…In [24] a customized background surface with grid lines is used to estimate pill sizes with features including color, shape, length to width ratio, size and texture. In [25] a customized background surface is used, again to enable the estimation of pill size. Additional features used include Hu moments as shape descriptors and color.…”

Section: Introductionmentioning

confidence: 99%

The national library of medicine pill image recognition challenge: An initial report

Yaniv

Faruque

Howe

et al. 2016

2016 IEEE Applied Imagery Pattern Recognition Workshop (AIPR)

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In January 2016 the U.S. National Library of Medicine announced a challenge competition calling for the development and discovery of high-quality algorithms and software that rank how well consumer images of prescription pills match reference images of pills in its authoritative RxIMAGE collection. This challenge was motivated by the need to easily identify unknown prescription pills both by healthcare personnel and the general public. Potential benefits of this capability include confirmation of the pill in settings where the documentation and medication have been separated, such as in a disaster or emergency; and confirmation of a pill when the prescribed medication changes from brand to generic, or for any other reason the shape and color of the pill change. The data for the competition consisted of two types of images, high quality macro photographs, reference images, and consumer quality photographs of the quality we expect users of a proposed application to acquire. A training dataset consisting of 2000 reference images and 5000 corresponding consumer quality images acquired from 1000 pills was provided to challenge participants. A second dataset acquired from 1000 pills with similar distributions of shape and color was reserved as a segregated testing set. Challenge submissions were required to produce a ranking of the reference images, given a consumer quality image as input. Determination of the winning teams was done using the mean average precision quality metric, with the three winners obtaining mean average precision scores of 0.27, 0.09, and 0.08. In the retrieval results, the correct image was amongst the top five ranked images 43%, 12%, and 11% of the time, out of 5000 query/consumer images. This is an initial promising step towards development of an NLM software system and application-programming interface facilitating pill identification. The training dataset will continue to be freely available online at: http://pir.nlm.nih.gov/challenge/submission.html.

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HelpmePills: A Mobile Pill Recognition Tool for Elderly Persons

Cited by 29 publications

References 9 publications

Assisted Medication Management in Elderly Care Using Miniaturised Near-Infrared Spectroscopy

Assisted Medication Management in Elderly Care Using Miniaturised Near-Infrared Spectroscopy

iPill: An iOS Mobile Application for Automatic Pill Identification Based on Image Processing Technique and Eigenface Algorithm

The national library of medicine pill image recognition challenge: An initial report

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