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Project

Automated VIdeo-based assessment of DYSkinesia in cerebral palsy using markerless pose estimation and machine learning (AVI-DYS) (AVI-DYS)

Movement disorders in dyskinetic cerebral palsy (DCP) are associated with impaired muscle tone regulation and interfere with intentional movements. To treat movement disorders invasive neuromodulation treatments are increasingly applied within DCP. Effective monitoring is extremely important for the indication, evaluation and dosing of these interventions. Current methods to assess movement disorders in DCP are insufficient and time-consuming. In addition hospital measurements are not representative of the real-word situation, as movement disorders may vary considerably during the day, and increase with emotions or pain. Therefore, the aim of the study is to develop an objective measurement technique that can be used in everyday situations. We suggest using supervised machine learning to automatically classifies dyskinetic movement patterns and assess severity, using data extracted by markerless motion from videos. Markerless motion tracking runs already automatically for common movements of abled-bodied persons. We will 1) re-train an existing human model on 4000 unique videos of 120 children with DCP performing everyday tasks 2) train algorithms that maps features from the videos to clinical scoring 3) assess psychometric measurement properties of the best performing algorithms on data not used in the algorithm development and 4) assess feasibility on home-based videos. The study is a first step to develop an objective and easily applicable movement assessment tool for DCP.

Date:1 Nov 2021 →  31 Oct 2023
Keywords:movement disorders, cerebral palsy, assessment, dystonia, choreo-athetosis, computer vision, machine learning, feasibility, human-pose estimation
Disciplines:Biomechanics, Paediatrics