It is the story of a mom, who after having her child, realized that she had developed the maternal capacity to understand her baby’s intentions by analyzing these crying and behavior.
Soon, she was able to understand if her baby wanted to drink, to eat, or other informal information that not every people knows how to understand.
The rest of the story made sense to her: to develop an application and to share her maternal experience with the other parents: she called it “Chatterbaby”.
Thanks to the AI and machine learning, as well as the parents’ data-sharing agreement of more than already 1500 babies, the application presents an innovative solution for the young parents worried about the supreme well-being of their babies. More in detail, the application’s algorithm uses sound processing and machine intelligence to compare acoustic signals to the cries present in their database. They learned for example by the time that cries due to pain were generally constituted with more energy and fewer silences, which can seem logical but can really help on the way to arrest the correlation between the tears of the children and their development.
Aware that there is nothing worse than not to understand why a baby is crying, the team behind the project managed to present a promising application which seems at least effective, and moreover, respective of people’s data.
Namely that its evolution, thanks to the community’s data, can take it even further, and could even be in the future a solid base to apprehend and better detect young babies’ autism.
A disease still too long to detect, which reduces the care opportunities and adaptation for babies.
So, we can only support this initiative, which could prove to offer opportunities even beyond its original intention: the translation of the baby’s language.

A propos de Benjamin JUSTER