Nutritional assessment of children under five based on anthropometric measurements with image processing techniques

Victor A. Ayma; Victor H. Ayma; Armando Torre; Lizette Ganoza

doi:10.1109/ANDESCON.2016.7836265

Nutritional assessment of children under five based on anthropometric measurements with image processing techniques

Victor A. Ayma, Victor H. Ayma, Armando Torre, Lizette Ganoza

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

Nutritional assessment is an important evaluation to prevent and control malnutrition, which is one of the main causes associated with child mortality. Weight and height are the most frequently measured morphological traits which in combination with child's gender and age, generates anthropometric indices to establish child's nutritional status. Nevertheless, accomplishment of this task in rural areas is difficult because of complications to transport bulky and heavy equipment, which must be properly and adequately calibrated. This work proposed a novel approach to perform nutritional assessments of children under five, through a system focused on the estimation of anthropometric indices, based on the measurements obtained from a set of body part images and its relations with child's gender and age. The results showed that sensitivity and specificity for the anthropometric indicators, ranged from 66% to 100% and 88% to 100%, respectively. Moreover, overall accuracies were over 85% up to 100%. Additionally, the experiments conducted shown our method as a viable solution to perform nutritional evaluations via accurate anthropometric index estimations.

Original language	English
Title of host publication	Proceedings of the 2016 IEEE ANDESCON
Place of Publication	United States
Publisher	IEEE Xplore
ISBN (Electronic)	9781509025312
ISBN (Print)	9781509035694
DOIs	https://doi.org/10.1109/ANDESCON.2016.7836265
State	Published - 27 Jan 2017
Externally published	Yes
Event	SIBGRAPI Conference on Graphics, Patterns and Images - Sao Paulo, Brazil Duration: 4 Oct 2016 → 7 Oct 2016 Conference number: 29 http://gibis.unifesp.br/sibgrapi16/

Publication series

Name	Brazilian Symposium on Computer Graphics and Image Processing
ISSN (Print)	1530-1834
ISSN (Electronic)	2377-5416

Conference

Conference	SIBGRAPI Conference on Graphics, Patterns and Images
Abbreviated title	SIBGRAPI
Country/Territory	Brazil
City	Sao Paulo
Period	4/10/16 → 7/10/16
Internet address	http://gibis.unifesp.br/sibgrapi16/

Bibliographical note

Publisher Copyright: © 2016 IEEE.

SIBGRAPI - Conference on Graphics, Patterns and Images is an international conference annually promoted by the Brazilian Computer Society (SBC).

Date Added to IEEE Xplore: 02 February 2017.

Keywords

image processing
machine learning
malnutrition
neural networks
nutrition assessment

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1109/ANDESCON.2016.7836265

Cite this

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title = "Nutritional assessment of children under five based on anthropometric measurements with image processing techniques",

abstract = "Nutritional assessment is an important evaluation to prevent and control malnutrition, which is one of the main causes associated with child mortality. Weight and height are the most frequently measured morphological traits which in combination with child's gender and age, generates anthropometric indices to establish child's nutritional status. Nevertheless, accomplishment of this task in rural areas is difficult because of complications to transport bulky and heavy equipment, which must be properly and adequately calibrated. This work proposed a novel approach to perform nutritional assessments of children under five, through a system focused on the estimation of anthropometric indices, based on the measurements obtained from a set of body part images and its relations with child's gender and age. The results showed that sensitivity and specificity for the anthropometric indicators, ranged from 66% to 100% and 88% to 100%, respectively. Moreover, overall accuracies were over 85% up to 100%. Additionally, the experiments conducted shown our method as a viable solution to perform nutritional evaluations via accurate anthropometric index estimations.",

keywords = "image processing, machine learning, malnutrition, neural networks, nutrition assessment",

author = "Ayma, {Victor A.} and Ayma, {Victor H.} and Armando Torre and Lizette Ganoza",

note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; SIBGRAPI Conference on Graphics, Patterns and Images ; Conference date: 04-10-2016 Through 07-10-2016",

year = "2017",

month = jan,

day = "27",

doi = "10.1109/ANDESCON.2016.7836265",

language = "Ingl{\'e}s",

isbn = "9781509035694",

series = "Brazilian Symposium on Computer Graphics and Image Processing ",

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booktitle = "Proceedings of the 2016 IEEE ANDESCON",

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Ayma, VA, Ayma, VH, Torre, A & Ganoza, L 2017, Nutritional assessment of children under five based on anthropometric measurements with image processing techniques. in Proceedings of the 2016 IEEE ANDESCON., 7836265, Brazilian Symposium on Computer Graphics and Image Processing , IEEE Xplore, United States, SIBGRAPI Conference on Graphics, Patterns and Images , Sao Paulo, Brazil, 4/10/16. https://doi.org/10.1109/ANDESCON.2016.7836265

Nutritional assessment of children under five based on anthropometric measurements with image processing techniques. / Ayma, Victor A.; Ayma, Victor H.; Torre, Armando et al.
Proceedings of the 2016 IEEE ANDESCON. United States: IEEE Xplore, 2017. 7836265 (Brazilian Symposium on Computer Graphics and Image Processing ).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Nutritional assessment of children under five based on anthropometric measurements with image processing techniques

AU - Ayma, Victor A.

AU - Ayma, Victor H.

AU - Torre, Armando

AU - Ganoza, Lizette

N1 - Conference code: 29

PY - 2017/1/27

Y1 - 2017/1/27

N2 - Nutritional assessment is an important evaluation to prevent and control malnutrition, which is one of the main causes associated with child mortality. Weight and height are the most frequently measured morphological traits which in combination with child's gender and age, generates anthropometric indices to establish child's nutritional status. Nevertheless, accomplishment of this task in rural areas is difficult because of complications to transport bulky and heavy equipment, which must be properly and adequately calibrated. This work proposed a novel approach to perform nutritional assessments of children under five, through a system focused on the estimation of anthropometric indices, based on the measurements obtained from a set of body part images and its relations with child's gender and age. The results showed that sensitivity and specificity for the anthropometric indicators, ranged from 66% to 100% and 88% to 100%, respectively. Moreover, overall accuracies were over 85% up to 100%. Additionally, the experiments conducted shown our method as a viable solution to perform nutritional evaluations via accurate anthropometric index estimations.

AB - Nutritional assessment is an important evaluation to prevent and control malnutrition, which is one of the main causes associated with child mortality. Weight and height are the most frequently measured morphological traits which in combination with child's gender and age, generates anthropometric indices to establish child's nutritional status. Nevertheless, accomplishment of this task in rural areas is difficult because of complications to transport bulky and heavy equipment, which must be properly and adequately calibrated. This work proposed a novel approach to perform nutritional assessments of children under five, through a system focused on the estimation of anthropometric indices, based on the measurements obtained from a set of body part images and its relations with child's gender and age. The results showed that sensitivity and specificity for the anthropometric indicators, ranged from 66% to 100% and 88% to 100%, respectively. Moreover, overall accuracies were over 85% up to 100%. Additionally, the experiments conducted shown our method as a viable solution to perform nutritional evaluations via accurate anthropometric index estimations.

KW - image processing

KW - machine learning

KW - malnutrition

KW - neural networks

KW - nutrition assessment

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DO - 10.1109/ANDESCON.2016.7836265

M3 - Contribución a la conferencia

AN - SCOPUS:85015197178

SN - 9781509035694

T3 - Brazilian Symposium on Computer Graphics and Image Processing

BT - Proceedings of the 2016 IEEE ANDESCON

PB - IEEE Xplore

CY - United States

T2 - SIBGRAPI Conference on Graphics, Patterns and Images

Y2 - 4 October 2016 through 7 October 2016

ER -

Nutritional assessment of children under five based on anthropometric measurements with image processing techniques

Abstract

Publication series

Conference

Bibliographical note

Keywords

UN SDGs

Access to Document

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