Background and activities

  • Pursues PhD in Medical Technology (2018-)
  • PhD project: "Explainable AI-based Movement Analysis to Promote Infant Care"
  • Project in collaboration between NTNU (IKOM, INB and IDI), St. Olavs Hospital (Clinic of Clinical Services and Department of Neonatology) and Norwegian Open AI Lab
  • Tracking of body parts from video recordings
  • Video-based prediction of movement disorders
  • Markerless gait analysis of children with cerebral palsy
  • Kinematic analysis of elite ski jumpers in collaboration with Centre for Elite Sports Research and Olympiatoppen
  • Co-inventor of smartphone app for early detection of cerebral palsy
  • Co-supervisor of Master students in Computer Science


  • Deep Learning
  • Computer Vision
  • Convolutional Neural Networks
  • Human Pose Estimation
  • Supervised learning
  • Transfer learning
  • Explainable Artificial Intelligence


  • Master of Science in Computer Science from NTNU (2013-2018)
  • Specialization in Artificial Intelligence
  • Master's Thesis on tracking of infant movements in videos using Deep Learning
  • Internships at Telenor Research and Skatteetatens IT- og servicepartner


Scientific, academic and artistic work

Displaying a selection of activities. See all publications in the database


  • Groos, Daniel; Aurlien, Kristian; Ramampiaro, Heri; Ihlen, Espen Alexander F.; deRegnier, RA; Peyton, Colleen; Silberg, Inger Elisabeth; Songstad, Nils Thomas; Thomas, Niranjan; Adde, Lars. (2019) Deep Learning‐based infant motion tracking facilitating early detection of cerebral palsy. Developmental Medicine & Child Neurology. vol. 61 (S2).


  • Groos, Daniel; Aurlien, Kristian. (2018) Infant body part tracking in videos using Deep Learning. 2018.