Zoundream's AI-Based Cry Analysis: A Breakthrough in Infant Care and Family Well-being.
Zoundream technology has been built based on over five years of research, in collaboration with pediatricians, clinical researchers, and hospitals around the world.
The technology has been used by thousands of families with their babies, for over a milion hours of use, inside families' homes.
The collaboration with some hospitals also led to some scientific papers being published on top tier clinical journals. An extract (and link to full articles) can be found below:
These findings will contribute to a better understanding of the interpretation of cry never accomplished until now, emphasizing the clinical potential of cry analysis as an objective non-invasive AI-based tool to enhance the parent-child relationship and ensure family well-being and the proper development of the newborn.
Multi modal Analysis of Infant Cry Types Characterization: Acoustics, Body Language and Brain Signals
This pioneer multimodal study in collaboration with Hospital Clínic (Barcelona, Spain) demonstrates that infant cry expression triggers a complex communicative process that involves brain and behavioral patterns helping to differentiate cry types associated with various needs or emotions in the newborn, such as hunger, sleepiness, discomfort, or irritability, among others.
These findings will contribute to a better understanding of the interpretation of cry never accomplished until now, emphasizing the clinical potential of cry analysis as an objective non-invasive AI-based tool to enhance the parent-child relationship and ensure family well-being and the proper development of the newborn.
How can cry acoustics associate newborns’ distress levels with neurophysiological and behavioral signals?
In this innovative research, conducted through a clinical study at Hospital Clínic (Barcelona, Spain), we used for the first time cry audio analysis as a prospective vocal biomarker of newborns' distress states cross-validated with behavioral and brain signals, being a valuable AI-based tool objectively and quantitatively supporting the assessment of the arousal state of the infant in the future neonatology.
