MERON is using facial recognition technology to detect malnutrition in children (aged 0-5) during humanitarian emergencies. MERON uses an algorithm to analyze facial curvature and assesses other non-traditional markers to estimate body mass index. For child safety, the actual image is not stored, merely key points of the face that are intended to be used to create a facial map. This information can assist in identifying the children who need nutrition support and in getting it to them in a timely manner. MERON is seeks to be a scalable alternative to the Mid-Upper Arm Circumference (MUAC) rapid assessment method, which requires training and supervision to reduce errors in its application. The MERON app was partly funded by the UNICEF Innovation Fund. The anthropometric data and photos used to train the model were collected in collaboration with the Ministry of Health and UNICEF Kenya.
The original content of this case is from Oxford Initiative on AI×SDGs (2018-2022) which was a research project at the University of Oxford, directed by Prof. Luciano Floridi and Prof. Mariarosaria Taddeo. Its goal was to determine how artificial intelligence (AI) has been and can be used to support and advance the United Nations Sustainable Development Goals (SDGs). One of the deliverables was a curated, open, and fully searchable collection of international projects that use AI to support one or more of the SDGs. The content of that collection is now hosted here. We thank Prof. Floridi, Prof. Taddeo and their research team for the collaboration. Descriptions and functionalities have been extended to adapt the original content to the AI for SDGs Think Tank Observatory.
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