Daniel Groos

Daniel Groos is a Researcher at the Department of Computer Science, NTNU, specializing in the development of machine learning models for medical and sports-related motion analysis. His work focuses on applying deep learning techniques to video-based movement analysis for early detection of cerebral palsy in infants and performance analysis in elite sports.

Education: PhD in Medical Technology (NTNU, 2018-2022), MSc in Computer Science with specialization in AI (NTNU, 2013-2018).

Research interests include interdisciplinary collaborations with St. Olavs Hospital and Norwegian Open AI Lab. Key topics are deep learning applications in healthcare, computer vision for movement analysis, and sports biomechanics.

Publications emphasize automated clinical analysis, video-based diagnostics, and human pose estimation. Notable projects include a deep learning method for cerebral palsy prediction and motion tracking systems for elite ski jumpers.

Collaborations with institutions like the Centre for Elite Sports Research and Olympiatoppen highlight his work in sports performance analysis. No formal scientific awards listed but active in academic outreach with lectures at European conferences on childhood disability and movement analysis.