96 citations · 151 across the 5 of their papers we have counts for
5 papers
Towards Biomarker Discovery for Early Cerebral Palsy Detection: Evaluating Explanations Through Kinematic Perturbations
Kimji N. Pellano, Inga Strümke, Daniel Groos +3
Cerebral Palsy (CP) is a prevalent motor disability in children, for which early detection can significantly improve treatment outcomes. While skeleton-based Graph Convolutional Ne…
Choose Your Explanation: A Comparison of SHAP and GradCAM in Human Activity Recognition
Felix Tempel, Daniel Groos, Espen Alexander F. Ihlen +2
Explaining machine learning (ML) models using eXplainable AI (XAI) techniques has become essential to make them more transparent and trustworthy. This is especially important in hi…
Evaluating Explainable AI Methods in Deep Learning Models for Early Detection of Cerebral Palsy
Kimji N. Pellano, Inga Strümke, Daniel Groos +2
Early detection of Cerebral Palsy (CP) is crucial for effective intervention and monitoring. This paper tests the reliability and applicability of Explainable AI (XAI) methods usin…
Towards human-level performance on automatic pose estimation of infant spontaneous movements
Daniel Groos, Lars Adde, Ragnhild Støen +2
Assessment of spontaneous movements can predict the long-term developmental disorders in high-risk infants. In order to develop algorithms for automated prediction of later disorde…
EfficientPose: Scalable single-person pose estimation
Daniel Groos, Heri Ramampiaro, Espen A. F. Ihlen
Single-person human pose estimation facilitates markerless movement analysis in sports, as well as in clinical applications. Still, state-of-the-art models for human pose estimatio…