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Agreement between Azure Kinect and Marker-Based Motion Analysis during Functional Movements: A Feasibility Study.

Sungbae Jo1, Sunmi Song2,3,4, Junesun Kim2,3,4,5

  • 1Department of Physical Therapy, College of Health Science, Sahmyook University, Seoul 01795, Republic of Korea.

Sensors (Basel, Switzerland)
|December 23, 2022
PubMed
Summary

The Azure Kinect shows moderate to good agreement with marker-based motion analysis for visible body parts during functional movements. However, tracking accuracy for hidden body segments remains a concern.

Keywords:
3D motion analysisactivities of daily livingdepth sensormotion capture

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Area of Science:

  • Biomechanics
  • Human Movement Analysis
  • Wearable Technology

Background:

  • Marker-based motion analysis is the gold standard for quantifying human movement.
  • Azure Kinect offers a markerless motion capture alternative.
  • Investigating the agreement between these systems is crucial for practical applications.

Purpose of the Study:

  • To assess the concurrent validity of Azure Kinect compared to marker-based motion analysis.
  • To evaluate the accuracy of Azure Kinect during various functional movements.
  • To identify limitations of Azure Kinect in motion capture.

Main Methods:

  • Twelve healthy adults performed six functional tasks (squats, lunges, reaches).
  • Movement data were captured simultaneously using Azure Kinect and 12 infrared cameras.
  • Bland-Altman plots and scatter plots were used to compare joint angles.

Main Results:

  • Moderate to high concurrent validity was found for visible hip and knee angles during squats.
  • Shoulder angles showed moderate (forward reach) to excellent (lateral reach) validity.
  • Lunge analysis revealed variable validity, with hidden joint angles showing poor accuracy.

Conclusions:

  • Azure Kinect demonstrates moderate to good agreement with marker-based systems for visible body segments.
  • Accuracy decreases significantly for occluded or hidden body parts.
  • Further improvements are needed for reliable tracking of non-visible segments.