Ubenwa: Cry-based Diagnosis of Birth Asphyxia
Not on the map. Why?
Abstract (arXiv)
Every year, 3 million newborns die within the first month of life. Birth asphyxia and other breathing-related conditions are a leading cause of mortality during the neonatal phase. Current diagnostic methods are too sophisticated in terms of equipment, required expertise, and general logistics. Consequently, early detection of asphyxia in newborns is very difficult in many parts of the world, especially in resource-poor settings. We are developing a machine learning system, dubbed Ubenwa, which enables diagnosis of asphyxia through automated analysis of the infant cry. Deployed via smartphone and wearable technology, Ubenwa will drastically reduce the time, cost and skill required to make accurate and potentially life-saving diagnoses.
Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). First posted on arXiv 17 Nov 2017.
Builds on
No reference list yet.
Built on by
Citing papers per year: 2023: 3; 2024: 1.
4 papers.
- 2023Self-supervised learning for infant cry analysiscited by 4
- 2023Machine Learning Research Trends in Africa: A 30 Years Overview with Bibliometric Analysis Reviewcited by 3
- 2023CryCeleb: A Speaker Verification Dataset Based on Infant Cry Soundscited by 3
- 2024HumekaFL: Automated Detection of Neonatal Asphyxia Using Federated Learningcited by 0
- 2024HumekaFL: Automated Detection of Neonatal Asphyxia Using Federated Learning2 Dec 2024
- 2023Self-supervised learning for infant cry analysis2 May 2023
- 2023CryCeleb: A Speaker Verification Dataset Based on Infant Cry Sounds1 May 2023
- 2023Machine Learning Research Trends in Africa: A 30 Years Overview with Bibliometric Analysis Review15 Apr 2023