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Azure Kinect performance evaluation for human motion and upper limb biomechanical analysis.

Cristina Brambilla1, Roberto Marani1, Laura Romeo1,2

  • 1Institute of Intelligent Industrial Systems and Technologies for Advanced Manufacturing (STIIMA), Italian Council of National Research (CNR), Italy.

Heliyon
|November 29, 2023
PubMed
Summary
This summary is machine-generated.

Marker-less human motion tracking using the Microsoft Azure Kinect sensor shows promise for medical applications. Frontal camera positioning enhances accuracy, while occlusions can decrease it, but overall feasibility is demonstrated.

Keywords:
Azure KinectBiomechanicsHuman trackingKinematicsUpper limbVicon

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Area of Science:

  • Biomechanics
  • Human Motion Analysis
  • Medical Technology

Background:

  • Marker-based optoelectronic systems are the gold standard for human motion tracking but are often impractical in clinical settings.
  • Marker-less sensors offer a cost-effective, non-invasive alternative, though their accuracy is sensitive to environmental factors and sensor placement.
  • Previous research explored marker-less systems, necessitating further investigation into newer technologies like the Microsoft Azure Kinect.

Purpose of the Study:

  • To evaluate the performance of the Microsoft Azure Kinect sensor for kinematic and dynamic measurements in human motion tracking.
  • To compare the Azure Kinect's accuracy against a gold-standard Vicon marker-based system for upper body movements.
  • To determine optimal camera positioning and assess the impact of lighting and occlusions on tracking accuracy.

Main Methods:

  • Twenty-five healthy volunteers participated in the study.
  • The Microsoft Azure Kinect sensor was tested in various positions and lighting conditions.
  • Lower limb occlusions were introduced to assess their effect on upper limb tracking accuracy.
  • Kinematic, dynamic, and motor control parameters were measured and statistically analyzed.

Main Results:

  • The frontal camera position yielded the most reliable tracking data.
  • Lighting conditions had a negligible impact on the tracking accuracy.
  • Occlusion of the lower limbs significantly degraded the accuracy of upper limb motion tracking.

Conclusions:

  • The Microsoft Azure Kinect sensor is a feasible tool for biomechanical monitoring of human motion.
  • Optimal camera placement, specifically frontal positioning, is crucial for maximizing accuracy.
  • Environmental factors like lighting have minimal impact, but occlusions require careful consideration for reliable application.