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

2026-02-13 · Source: Computer Vision and Pattern Recognition · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Computer Vision, Data Science & Analytics · Depth: Advanced, quick

Summary

A study validated a clinically accessible method for quantifying Upper Extremity Reachable Workspace (UERW) using a single (monocular) camera and AI-driven Markerless Motion Capture (MMC). Nine unimpaired adults performed a standardized UERW task, reaching virtual targets while simultaneously recorded by a marker-based system and eight FLIR cameras. Monocular video analysis was conducted on two camera views: frontal and offset. The frontal camera configuration showed strong agreement with the marker-based reference, with a mean bias of 0.61 \u00b1 0.12 % reachspace reached per octant. Conversely, the offset camera view underestimated the workspace by -5.66 \u00b1 0.45 % reachspace reached. This research supports the feasibility of a frontal monocular camera for UERW assessment, especially for anterior workspace evaluation, marking the first validation of monocular MMC for this task.

Key takeaway

For physical therapists or rehabilitation engineers seeking to implement quantitative upper extremity mobility assessments, this study indicates that a frontal monocular camera setup with AI-driven Markerless Motion Capture is a validated, practical option. You should prioritize a frontal camera configuration for optimal accuracy, particularly when evaluating anterior workspace, to reduce technical complexity and overhead in clinical settings.

Key insights

Monocular AI-driven markerless motion capture offers a clinically viable, low-overhead method for upper extremity reachable workspace assessment.

Principles

• Frontal camera views yield higher accuracy.
• Offset camera views can underestimate reachspace.

Method

Participants perform a standardized UERW task, reaching VR targets. Movements are captured by both marker-based and monocular AI-driven MMC systems, then compared for validation.

In practice

• Use frontal camera for UERW assessment.
• Prioritize anterior workspace evaluation.

Topics

• Markerless Motion Capture
• Upper Extremity Reachable Workspace
• Monocular Computer Vision
• Biomechanical Assessment
• Clinical Motion Analysis

Best for: Computer Vision Engineer, AI Scientist, AI Researcher, Research Scientist, Domain Expert

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Editorial summary, takeaway, and curation by AIssential. Original article published by Computer Vision and Pattern Recognition.