Benoit L. Marteau

This study validates a clinically accessible approach for quantifying the Upper Extremity Reachable Workspace (UERW) using monocular AI-driven Markerless Motion Capture (MMC). Objective validation of such techniques for clinically oriented tasks is essential to support their adoption in clinical motion analysis. Nine adults without impairments performed the standardized UERW task, reaching targets distributed across a virtual sphere centered on the torso and displayed via VR headset. Movements were simultaneously captured with a marker-based system and eight FLIR cameras; monocular analysis was applied to two videos representing frontal and offset camera configurations. Agreement was assessed by comparing the percentage workspacereached across six of eight workspace octants between the systems. The frontal camera demonstrated strong agreement with the marker-based reference (mean bias: $0.61\pm 0.12$% reachspace per octant), whereas the offset view underestimated workspace reached $-5.66\pm 0.45$%. Depth-related errors in the frontal configuration were confined to posterior octants, whereas the offset view introduced inaccuracies in both contralateral and posterior octants. These findings support the feasibility of a frontal monocular camera for UERW assessment, particularly for anterior workspace evaluation. While posterior accuracy remains limited by depth estimation and anatomical occlusion errors, the overall results demonstrate clinical potential for practical, monocular-camera assessments.

To validate a clinically accessible approach for quantifying the Upper Extremity Reachable Workspace (UERW) using a single (monocular) camera and Artificial Intelligence (AI)-driven Markerless Motion Capture (MMC) for biomechanical analysis. Objective assessment and validation of these techniques for specific clinically oriented tasks are crucial for their adoption in clinical motion analysis. AI-driven monocular MMC reduces the barriers to adoption in the clinic and has the potential to reduce the overhead for analysis of this common clinical assessment. Nine adult participants with no impairments performed the standardized UERW task, which entails reaching targets distributed across a virtual sphere centered on the torso, with targets displayed in a VR headset. Movements were simultaneously captured using a marker-based motion capture system and a set of eight FLIR cameras. We performed monocular video analysis on two of these video camera views to compare a frontal and offset camera configurations. The frontal camera orientation demonstrated strong agreement with the marker-based reference, exhibiting a minimal mean bias of $0.61 \pm 0.12$ \% reachspace reached per octanct (mean $\pm$ standard deviation). In contrast, the offset camera view underestimated the percent workspace reached ($-5.66 \pm 0.45$ \% reachspace reached). Conclusion: The findings support the feasibility of a frontal monocular camera configuration for UERW assessment, particularly for anterior workspace evaluation where agreement with marker-based motion capture was highest. The overall performance demonstrates clinical potential for practical, single-camera assessments. This study provides the first validation of monocular MMC system for the assessment of the UERW task. By reducing technical complexity, this approach enables broader implementation of quantitative upper extremity mobility assessment.