Towards Automated and Marker-less Parkinson Disease Assessment: Predicting UPDRS Scores using Sit-stand videos

@article{Mehta2021TowardsAA,
  title={Towards Automated and Marker-less Parkinson Disease Assessment: Predicting UPDRS Scores using Sit-stand videos},
  author={Deval Mehta and Umar Asif and Tian Hao and Erhan Bilal and Stefan von Cavallar and Stefan Harrer and Jeffrey Rogers},
  journal={2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)},
  year={2021},
  pages={3836-3844}
}
  • Deval Mehta, U. Asif, +4 authors Jeffrey Rogers
  • Published 2021
  • Computer Science
  • 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
This paper presents a novel deep learning enabled, video based analysis framework for assessing the Unified Parkinson’s Disease Rating Scale (UPDRS) that can be used in the clinic or at home. We report results from comparing the performance of the framework to that of trained clinicians on a population of 32 Parkinson’s disease (PD) patients. In-person clinical assessments by trained neurologists are used as the ground truth for training our framework and for comparing the performance. We find… Expand

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