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Precision Balance Assessment in Parkinson's Disease: Utilizing Vision-Based 3D Pose Tracking for Pull Test Analysis.

Nina EllrichKasimir NiermeyerDaniela PetoJulian DeckerUrban M FietzekSabrina KatzdoblerGünter U HöglingerKlaus JahnAndreas ZwergalMax Wuehr
Published in: Sensors (Basel, Switzerland) (2024)
Postural instability is a common complication in advanced Parkinson's disease (PD) associated with recurrent falls and fall-related injuries. The test of retropulsion, consisting of a rapid balance perturbation by a pull in the backward direction, is regarded as the gold standard for evaluating postural instability in PD and is a key component of the neurological examination and clinical rating in PD (e.g., MDS-UPDRS). However, significant variability in test execution and interpretation contributes to a low intra- and inter-rater test reliability. Here, we explore the potential for objective, vision-based assessment of the pull test (vPull) using 3D pose tracking applied to single-sensor RGB-Depth recordings of clinical assessments. The initial results in a cohort of healthy individuals ( n = 15) demonstrate overall excellent agreement of vPull-derived metrics with the gold standard marker-based motion capture. Subsequently, in a cohort of PD patients and controls ( n = 15 each), we assessed the inter-rater reliability of vPull and analyzed PD-related impairments in postural response (including pull-to-step latency, number of steps, retropulsion angle). These quantitative metrics effectively distinguish healthy performance from and within varying degrees of postural impairment in PD. vPull shows promise for straightforward clinical implementation with the potential to enhance the sensitivity and specificity of postural instability assessment and fall risk prediction in PD.
Keyphrases
  • healthcare
  • high resolution
  • primary care
  • ejection fraction
  • machine learning
  • risk assessment
  • optical coherence tomography
  • quality improvement
  • deep learning
  • high speed