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Related Experiment Video

Updated: Jun 13, 2026

Kinematic Analysis Using 3D Motion Capture of Drinking Task in People With and Without Upper-extremity Impairments
08:45

Kinematic Analysis Using 3D Motion Capture of Drinking Task in People With and Without Upper-extremity Impairments

Published on: March 28, 2018

Monocular Markerless Motion Capture Enables Quantitative Assessment of Upper Extremity Reachable Workspace.

Seth Donahue1,2, J D Peiffer3,4, R Tyler Richardson5

  • 1Shriners Children's Lexington, Lexington, KY 40508, USA.

Sensors (Basel, Switzerland)
|June 12, 2026
PubMed
Summary

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Sensors (Basel, Switzerland)·2025

AI-driven markerless motion capture (MMC) offers a viable method for quantifying upper extremity reachable workspace (UERW). A frontal camera view shows promising accuracy for clinical assessments, though posterior workspace evaluation requires further refinement.

Area of Science:

  • Biomechanics
  • Clinical Motion Analysis
  • Artificial Intelligence in Healthcare

Background:

  • Objective validation of clinical motion analysis techniques is crucial for widespread adoption.
  • Quantifying the Upper Extremity Reachable Workspace (UERW) is important for assessing functional movement.
  • Markerless motion capture (MMC) presents a potentially accessible alternative to marker-based systems.

Purpose of the Study:

  • To validate a clinically accessible, AI-driven monocular markerless motion capture (MMC) approach for quantifying the Upper Extremity Reachable Workspace (UERW).
  • To assess the accuracy of monocular camera configurations (frontal and offset) compared to a marker-based system for UERW assessment.

Main Methods:

  • Nine healthy adults performed a standardized UERW task using a VR headset.
Keywords:
Markerless Motion Captureartificial intelligenceclinical feasibilitymonocular camerareachable workspace

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Related Experiment Videos

Last Updated: Jun 13, 2026

Kinematic Analysis Using 3D Motion Capture of Drinking Task in People With and Without Upper-extremity Impairments
08:45

Kinematic Analysis Using 3D Motion Capture of Drinking Task in People With and Without Upper-extremity Impairments

Published on: March 28, 2018

Estimation of Contact Regions Between Hands and Objects During Human Multi-Digit Grasping
09:41

Estimation of Contact Regions Between Hands and Objects During Human Multi-Digit Grasping

Published on: April 21, 2023

  • Simultaneous motion capture using a marker-based system and eight FLIR cameras.
  • Monocular analysis applied to frontal and offset camera views; agreement assessed via workspace octant percentage reached.

    • Main Results:

    • The frontal monocular camera demonstrated strong agreement with the marker-based reference (mean bias: 0.61±0.12% per octant).
    • The offset camera view underestimated workspace reached (mean bias: -5.66±0.45%), with inaccuracies in contralateral and posterior octants.
    • Depth estimation and occlusion errors limited posterior accuracy in the frontal view.

    Conclusions:

    • A frontal monocular camera is feasible for UERW assessment, particularly for anterior workspace evaluation.
    • The AI-driven MMC approach shows clinical potential for practical, accessible motion analysis.
    • Further research is needed to address limitations in posterior workspace accuracy due to depth estimation and occlusion.