Nothobranchius furzeri, with its short lifespan, is a powerful model for aging research. While its complex behavior is influenced by age, the link between behavior and lifespan remains unclear due to difficulties in long-term monitoring. Traditional behavioral testing causes stress and creates large video files, limiting continuous observation. In this project we aim to develop a real-time system that can track hundreds of fish individually, directly in their tanks, eliminating handling and reducing data storage to under 20 MB per fish daily. By analyzing locomotion patterns, our goal is to predict biological age.

People:

Motahareh Ehsani