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Related Experiment Video

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Proposal for Post Hoc Quality Control in Instrumented Motion Analysis Using Markerless Motion Capture: Development

Hanna Marie Röhling1,2,3,4, Patrik Althoff1,2,3, Radina Arsenova1,2,3,5

  • 1Experimental and Clinical Research Center, a cooperation between the Max-Delbrück-Center for Molecular Medicine in the Helmholtz Association and the Charité - Universitätsmedizin Berlin, Berlin, Germany.

JMIR Human Factors
|April 1, 2022
PubMed
Summary
This summary is machine-generated.

A new quality control (QC) pipeline for markerless motion capture technology ensures reliable motor symptom assessment in movement disorders. This system enhances data quality for clinical use and future research.

Keywords:
gait analysisinstrumented motion analysismarkerless motion capturequality controlquality reportingvisual perceptive computing

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Area of Science:

  • Biomedical Engineering
  • Clinical Neurology
  • Rehabilitation Technology

Background:

  • Instrumented assessment of motor symptoms offers a promising extension to clinical evaluation for movement disorders.
  • Markerless motion capture technologies present a scalable, cost-effective solution for large-scale application.
  • Standardized tools are essential for quality control to integrate these technologies into clinical routine.

Purpose of the Study:

  • Develop a systematic quality control (QC) procedure for markerless motion capture data.
  • Implement and experimentally validate the QC procedure to identify quality concerns.
  • Rate the usability of motor task recordings using the developed QC pipeline.

Main Methods:

  • A post hoc QC pipeline was developed and evaluated on a large dataset of motor task recordings.
  • Recordings from healthy controls and individuals with multiple sclerosis were analyzed.
  • Two independent raters applied the pipeline, assessing usability and identifying technical/performance quality concerns.

Main Results:

  • The QC pipeline demonstrated user-friendliness and large-scale applicability.
  • Rater agreement on recording usability ranged from 71.5% to 92.3% across different motor tasks.
  • Satisfactory quality ratings were achieved in 39.6%-85.1% of recordings, with 5.0%-26.3% being discarded by both raters.

Conclusions:

  • A feasible and useful QC pipeline for clinical quality screening of markerless motion capture data was presented.
  • The study highlights the necessity of QC, even with standardized setups and training.
  • The QC process aids in data cleaning, quality assurance optimization, and development of automated QC approaches for improved kinematic data reliability.