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Daniel Groos

Daniel Groos

Senior Engineer, PhD
Department of Neuromedicine and Movement Science
Department of Computer Science

daniel.groos@ntnu.no
Google Scholar
About Publications Media

About

  • Development of machine learning models for video-based motion capture and early prediction of cerebral palsy
  • Interdisciplinary collaboration between NTNU (IKOM, INB and IDI), St. Olavs Hospital (Clinic of Clinical Services and Department of Neonatology) and Norwegian Open AI Lab
  • Movement analysis in golf and baseball with Initial Force and kinematic analysis of elite ski jumpers in collaboration with Centre for Elite Sports Research and Olympiatoppen
  • Inventor of deep learning methods for video-based motion capture and early detection of cerebral palsy

 

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

 

  • Doctoral degree in Medical Technology from NTNU (2018-2022)
  • Master of Science in Computer Science from NTNU (2013-2018)
  • Specialization in Artificial Intelligence

 

Competencies

  • Artificial intelligence
  • Computer Vision
  • Deep learning
  • Machine learning
  • Medical technology
  • Movement science
  • Neural networks
  • Sports science

Publications

  • Chronological
  • By category
  • See all publications in Cristin

2022

  • Groos, Daniel. (2022) Convolutional networks for video-based infant movement analysis: Towards objective prognosis of cerebral palsy from infant spontaneous movements. 2022. ISBN 978-82-326-6906-6. Doctoral theses at NTNU (2022:191).
    PhD thesis
  • Groos, Daniel; Adde, Lars; Aubert, Sindre Aarnes; Boswell, Lynn; De Regnier, Raye-Ann; Fjørtoft, Toril Larsson; Gaebler-Spira, Deborah; Haukeland, Andreas; Loennecken, Marianne; Msall, Michael; Møinichen, Unn Inger; Pascal, Aurelie; Peyton, Colleen; Ramampiaro, Heri; Schreiber, Michael D.; Silberg, Inger Elisabeth; Songstad, Nils Thomas; Thomas, Niranjan; van den Broeck, Christine; Øberg, Gunn Kristin; Ihlen, Espen Alexander F.; Støen, Ragnhild. (2022) Development and Validation of a Deep Learning Method to Predict Cerebral Palsy from Spontaneous Movements in Infants at High Risk. JAMA Network Open. volum 5 (7).
    Academic article
  • Groos, Daniel; Adde, Lars; Støen, Ragnhild; Ramampiaro, Heri; Ihlen, Espen Alexander F.. (2022) Towards human-level performance on automatic pose estimation of infant spontaneous movements. Computerized Medical Imaging and Graphics. volum 95.
    Academic article

2021

  • Adde, Lars; Brown, Annemette; van den Broeck, Christine; DeCoen, Kris; Eriksen, Beate Horsberg; Fjørtoft, Toril Larsson; Groos, Daniel; Ihlen, Espen Alexander F.; Osland, Siril; Pascal, Aurelie; Paulsen, Henriette; Skog, Ole-Morten; Sivertsen, Wiebke; Støen, Ragnhild. (2021) In-Motion-App for remote General Movement Assessment: a multi-site observational study. BMJ Open. volum 11 (3).
    Academic article
  • Elfmark, Ola; Ettema, Gertjan; Groos, Daniel; Ihlen, Espen Alexander F.; Velta, Rune; Haugen, Per; Bråten, Steinar; Gilgien, Matthias. (2021) Performance analysis in ski jumping with a differential global navigation satellite system and video-based pose estimation. Sensors. volum 21 (16).
    Academic article
  • Groos, Daniel; Adde, Lars; Aubert, Sindre Aarnes; Haukeland, Andreas; Ramampiaro, Heri; Støen, Ragnhild; Ihlen, Espen Alexander F.. (2021) Fully automated clinical movement analysis from videos using skeleton-based deep learning. Gait & Posture. volum 90.
    Summary/Abstract
  • Groos, Daniel; Adde, Lars; Støen, Ragnhild; Ramampiaro, Heri; Ihlen, Espen Alexander F.. (2021) New automatic, efficient, and highly precise tracking of infant spontaneous movements. Developmental Medicine & Child Neurology. volum 63.
    Summary/Abstract

2020

  • Groos, Daniel; Adde, Lars; Ihlen, Espen Alexander F.. (2020) Approaching human precision on automatic markerless tracking of human movements. Gait & Posture. volum 81.
    Summary/Abstract
  • Groos, Daniel; Ramampiaro, Heri; Ihlen, Espen Alexander F.. (2020) EfficientPose: Scalable single-person pose estimation. Applied intelligence (Boston).
    Academic article

2019

  • 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. volum 61.
    Summary/Abstract

2018

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

Scientific articles

  • Groos, Daniel; Adde, Lars; Aubert, Sindre Aarnes; Boswell, Lynn; De Regnier, Raye-Ann; Fjørtoft, Toril Larsson; Gaebler-Spira, Deborah; Haukeland, Andreas; Loennecken, Marianne; Msall, Michael; Møinichen, Unn Inger; Pascal, Aurelie; Peyton, Colleen; Ramampiaro, Heri; Schreiber, Michael D.; Silberg, Inger Elisabeth; Songstad, Nils Thomas; Thomas, Niranjan; van den Broeck, Christine; Øberg, Gunn Kristin; Ihlen, Espen Alexander F.; Støen, Ragnhild. (2022) Development and Validation of a Deep Learning Method to Predict Cerebral Palsy from Spontaneous Movements in Infants at High Risk. JAMA Network Open. volum 5 (7).
    Academic article
  • Groos, Daniel; Adde, Lars; Støen, Ragnhild; Ramampiaro, Heri; Ihlen, Espen Alexander F.. (2022) Towards human-level performance on automatic pose estimation of infant spontaneous movements. Computerized Medical Imaging and Graphics. volum 95.
    Academic article
  • Adde, Lars; Brown, Annemette; van den Broeck, Christine; DeCoen, Kris; Eriksen, Beate Horsberg; Fjørtoft, Toril Larsson; Groos, Daniel; Ihlen, Espen Alexander F.; Osland, Siril; Pascal, Aurelie; Paulsen, Henriette; Skog, Ole-Morten; Sivertsen, Wiebke; Støen, Ragnhild. (2021) In-Motion-App for remote General Movement Assessment: a multi-site observational study. BMJ Open. volum 11 (3).
    Academic article
  • Elfmark, Ola; Ettema, Gertjan; Groos, Daniel; Ihlen, Espen Alexander F.; Velta, Rune; Haugen, Per; Bråten, Steinar; Gilgien, Matthias. (2021) Performance analysis in ski jumping with a differential global navigation satellite system and video-based pose estimation. Sensors. volum 21 (16).
    Academic article
  • Groos, Daniel; Ramampiaro, Heri; Ihlen, Espen Alexander F.. (2020) EfficientPose: Scalable single-person pose estimation. Applied intelligence (Boston).
    Academic article

Journal publications

  • Groos, Daniel; Adde, Lars; Aubert, Sindre Aarnes; Haukeland, Andreas; Ramampiaro, Heri; Støen, Ragnhild; Ihlen, Espen Alexander F.. (2021) Fully automated clinical movement analysis from videos using skeleton-based deep learning. Gait & Posture. volum 90.
    Summary/Abstract
  • Groos, Daniel; Adde, Lars; Støen, Ragnhild; Ramampiaro, Heri; Ihlen, Espen Alexander F.. (2021) New automatic, efficient, and highly precise tracking of infant spontaneous movements. Developmental Medicine & Child Neurology. volum 63.
    Summary/Abstract
  • Groos, Daniel; Adde, Lars; Ihlen, Espen Alexander F.. (2020) Approaching human precision on automatic markerless tracking of human movements. Gait & Posture. volum 81.
    Summary/Abstract
  • 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. volum 61.
    Summary/Abstract

Report

  • Groos, Daniel. (2022) Convolutional networks for video-based infant movement analysis: Towards objective prognosis of cerebral palsy from infant spontaneous movements. 2022. ISBN 978-82-326-6906-6. Doctoral theses at NTNU (2022:191).
    PhD thesis
  • Groos, Daniel; Aurlien, Kristian. (2018) Infant body part tracking in videos using Deep Learning. 2018.
    Master thesis

Media

2021

  • Academic lecture
    Groos, Daniel; Adde, Lars; Aubert, Sindre Aarnes; Haukeland, Andreas; Ramampiaro, Heri; Støen, Ragnhild; Ihlen, Espen Alexander F.. (2021) Fully automated clinical movement analysis from videos using skeleton-based deep learning. ESMAC 2021 . European Society of Movement Analysis for Adults & Children; 2021-10-14 - 2021-10-15.
  • Academic lecture
    Groos, Daniel; Adde, Lars; Støen, Ragnhild; Ramampiaro, Heri; Ihlen, Espen Alexander F.. (2021) New automatic, efficient, and highly precise tracking of infant spontaneous movements. European Academy of Childhood Disability 2021 . European Academy of Childhood Disability; Virtual. 2021-05-20 - 2021-06-10.

2020

  • Academic lecture
    Groos, Daniel; Adde, Lars; Ihlen, Espen Alexander F.. (2020) Approaching human precision on automatic markerless tracking of human movements. Virtual ESMAC 2020 . European Society of Movement Analysis for Adults & Children; 2020-09-17.

2019

  • Poster
    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. European Academy of Childhood Disability 2019 . European Academy of Childhood Disability; Paris. 2019-05-23 - 2019-05-25.
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