Predicting human body dimensions from single images: A first step in automatic malnutrition detection

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

Abstract

Malnutrition in children accounts for 45% of child deaths globally. Automatic mal-nutrition detection from digital photos serves as a decision support tool for early detection of malnutrition in rural areas. We study the feasibility of estimating body-shape characteristics from images of human body shapes as a first step in automatic malnutrition detection. We generate multi-view images of male and female bodies from rendered digital 3D scans of human bodies.
Using convolutional neural networks (CNNs), we estimated waist circumference and body height with a mean absolute error of 59 mm and 9 mm, respectively. The estimation error of waist circumference depends on viewpoint. We conclude that automatic malnutrition detection from single images seems feasible, provided one or more suitable viewpoints are used
Original languageEnglish
Title of host publicationProceedings of the 1st International Conference on AI for People: Towards Sustainable AI
PublisherEAI
Publication statusAccepted/In press - 2021
EventINTERNATIONAL CONFERENCE ON AI for People: Towards Sustainable AI - Online
Duration: 20 Nov 202124 Nov 2021
https://aiforpeople.org/conference/

Conference

ConferenceINTERNATIONAL CONFERENCE ON AI for People: Towards Sustainable AI
Abbreviated titleCAIP'21
Period20/11/2124/11/21
Internet address

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