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Robust skeletal motion tracking using temporal and spatial synchronization of two video streams.

Vytautas Abromavičius1,2, Ervinas Gisleris2, Kristina Daunoravičienė3

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This study introduces a 3D skeleton tracking system for upper limb rehabilitation, improving depth accuracy with a dual-camera setup and error correction. It enhances remote monitoring and motor function evaluation, especially during occlusions.

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Area of Science:

  • Biomedical Engineering
  • Computer Vision
  • Rehabilitation Science

Background:

  • Skeletal motion tracking is crucial for rehabilitation monitoring and telerehabilitation.
  • Marker-based systems are accurate but costly; marker-less methods struggle with depth accuracy and occlusions.

Purpose of the Study:

  • To develop a cost-effective 3D human skeleton tracking system for upper limb rehabilitation exercises.
  • To improve depth estimation accuracy and tracking robustness, particularly under occlusion and non-frontal views.

Main Methods:

  • Integration of a 90° secondary camera to correct single-camera depth prediction inaccuracies.
  • Implementation of a linear regression-based depth error correction model.
  • Utilization of the Kalman filtering framework for temporal consistency and real-time interpolation.

Main Results:

  • Significant reduction in depth estimation errors for elbow and wrist joints (p < 0.001) compared to single-camera systems.
  • Improved tracking precision and robustness in scenarios with occlusions and varied perspectives.
  • Error margins reduced by up to 0.4 m through the dual-camera approach.

Conclusions:

  • The proposed system offers a cost-effective and scalable solution for remote patient monitoring in rehabilitation.
  • Enhanced skeletal tracking accuracy supports objective assessment of motor function recovery.
  • The method addresses key limitations of existing marker-less motion capture for home-based rehabilitation.