SEDFormer

Exciting news! Our paper "SEDformer: Path Signatures and Transformers to Predict Newborns Movement Symmetry" has been accepted at the International Joint Workshop of Artificial Intelligence for Healthcare (HC@AIxIA) and HYbrid Models for Coupling Deductive and Inductive ReAsoning (HYDRA), ECAI 2025.

This research introduces SEDformer, a FEDformer variant that integrates path signatures to enhance newborn movement symmetry prediction - a crucial step toward automated early screening tools for motor development disorders.

Thank you to the entire team for this interdisciplinary collaboration between applied mathematics, AI, and pediatric medicine.

 Rambaud, P., Rimmel, A., Trabelsi, I., Zini, J., Wodecki, A., Motte Signoret, E., Jouen, F., Tomasik, J., Bergounioux, J. (2025). SEDformer: Path Signatures and Transformers to Predict Newborns Movement Symmetry. International Joint Workshop of Artificial Intelligence for Healthcare (HC@AIxIA) and HYbrid Models for Coupling Deductive and Inductive ReAsoning (HYDRA)" ECAI 2025.