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 - Publisher Copyright:
© 2016 IEEE.
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
UR - http://www.scopus.com/inward/record.url?scp=85015197178&partnerID=8YFLogxK
U2 - 10.1109/ANDESCON.2016.7836265
DO - 10.1109/ANDESCON.2016.7836265
M3 - Conference contribution
AN - SCOPUS:85015197178
T3 - Proceedings of the 2016 IEEE ANDESCON, ANDESCON 2016
BT - Proceedings of the 2016 IEEE ANDESCON, ANDESCON 2016
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2016 IEEE ANDESCON, ANDESCON 2016
Y2 - 19 October 2016 through 21 October 2016
ER -