Table · dataset · 2026
Supplementary Material for: Motion Capture of Bronchoscopist Kinematics Using a Spatial Computing Device: A Methodological Proof-of-Concept Study
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<p dir="ltr"><b><i>Introduction</i></b>: Bronchoscopy is physically demanding, and studies of ergonomics and objective skill assessment require accurate, segment-resolved capture of the bronchoscopist’s hand and head movements.
Description
However, existing motion capture approaches typically rely on specialized laboratory infrastructure or wearable sensor arrays, limiting their routine use.</p><p dir="ltr"><b><i>Methods:</i></b> A board-certified pulmonologist performed 10 systematic bronchoscopies across the 18 named segments using an airway simulator and a single-use bronchoscope.
Procedures alternated between conditions without and with engagement of the bronchoscope’s insertion-tube rotation mechanism. A custom visionOS application running on Apple Vision Pro captured bilateral hand and head poses at 30 Hz, with synchronized voice-cue annotations for each segment.</p><p dir="ltr"><b><i>Results</i></b><b><i>:</i></b> All 180 expected segment insertions were recorded (94.4% via voice cues; 5.6% recovered through tracking-derived hold detection and excluded from per-insertion analyses).
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Intermittent tracking-freezing artifacts, in which a static pose was retained under a valid tracking flag, were detected by frame-to-frame immobility and excluded from the affected analyses. The mean per-insertion peak left-wrist rotation across all segments was 107.9° without and 72.6° with the rotation mechanism. The mean left-thumb path length within the wrist’s local reference frame was 3.82 ± 0.48 m per bronchoscopy, and the estimated number of bronchoscope lever operation cycles was 64.8 ± 7.9 per procedure.</p><p dir="ltr"><b><i>Conclusion:</i></b> In this proof-of-concept study, a single off-the-shelf head-mounted device enabled multi-channel capture of bronchoscopist kinematics during simulated bronchoscopy.
This approach provides a practical methodological foundation for future studies on bronchoscopy ergonomics, skill assessment, and AI-driven training data generation.</p>
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- DOI doi.org/10.6084/m9.figshare.33925600.v1 ↗
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- OAI-PMH record api.figshare.com/v2/oai?verb=GetRecord&metadataPrefix=oai_dc&identifier=oai%3Af… ↗
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