Developer Caleb Olson has unveiled a machine learning-based system that helps parents detect hunger in babies. The system pays attention to early signals of hunger, which portend crying.

Olson noticed that before crying, the baby less noticeably declares hunger. This usually manifests itself in the characteristic movement of the lips, hands and head. Parents do not notice these gestures and learn about hunger only after crying. Therefore, the developer had the idea to create an AI system that would monitor the child and report the appearance of these signals.

For the tracking device, the developer used a baby monitor with a built-in camera; the software part of the project was implemented using the Google MediaPipe library. Olson trained the system to recognize the baby's hunger gestures and assigned each gesture a priority. As a result, the system reads the characteristic behavior of the child and informs the parents that it is time to feed him. In this case, it turns out to almost completely prevent unnecessary crying, which makes the baby and parents nervous.

The developer spoke in detail about the project on his YouTube channel, and in the comments on the Hackaday portal he noted that he liked to implement the idea, and the system itself works consistently well. The enthusiast also said that he had already begun to think about how technology could help potty train his child.

This isn't Olson's first AI project to get a lot of publicity online. In January 2022, the developer introduced a dog feces tracking system in the backyard. This approach helped the enthusiast save time cleaning the lawn.

bbabo.Net