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Depth sensor technology in facial movement analysis: A comparative evaluation with marker-based motion analysis.

Beste Yilmaz1, Umut Ozsoy1, Yilmaz Yildirim1

  • 1Akdeniz University, Faculty of Medicine, Department of Anatomy, Antalya, Türkiye.

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Summary

Depth sensors offer reliable facial movement tracking but show biases for subtle expressions compared to marker-based systems. Algorithmic improvements are needed for clinical use of this non-invasive technology.

Keywords:
Depth SensorFacial Movement AnalysisKinect V2Marker-Based Motion AnalysisReliability

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Area of Science:

  • Biomedical Engineering
  • Human Movement Science
  • Computer Vision

Background:

  • Facial movement assessment is crucial in clinical diagnostics.
  • Marker-based motion analysis is the gold standard but is invasive and costly.
  • Depth sensors offer a non-invasive, potentially more accessible alternative.

Purpose of the Study:

  • To compare the reliability and agreement of depth sensor technology (Kinect-V2) with marker-based motion analysis for facial kinematics.
  • To evaluate the accuracy of depth sensors in capturing facial movements.

Main Methods:

  • 100 healthy participants performed six distinct facial movements.
  • Simultaneous recording using a marker-based system and a Kinect-V2 depth sensor.
  • Analysis included asymmetry, intra-method reliability (ICC), and agreement (Bland-Altman).

Main Results:

  • Depth sensors demonstrated strong intra-method reliability (ICC 0.61-0.85).
  • Bland-Altman analysis revealed biases and wider limits of agreement for depth sensors compared to marker-based systems, particularly for subtle expressions.
  • Asymmetry values differed between methods for smiling, eye-closure, and frowning.

Conclusions:

  • Depth sensors exhibit good intra-method reliability for facial movement analysis.
  • Current depth sensor technology shows significant biases and limitations in agreement for subtle facial expressions.
  • Further algorithmic refinement is necessary to enhance the clinical applicability of depth sensors for facial kinematics.