SynthesisFrontiers in digital health2026
Validations and applications of markerless motion capture using OpenCap: a scoping review.
Synthesis in Frontiers in digital health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
0 citing papers in PubMed.
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Recent advances in computer vision have substantially enhanced the accessibility and applicability of markerless motion capture systems. Among the available markerless motion capture platforms, OpenCap is a freely accessible, smartphone-based system that has received growing attention in biomechanics. Its applications have extended beyond controlled laboratory environments into field-based settings and beyond basic kinematic analysis to more complex clinical and sports-related applications. However, evidence regarding its concurrent validity, measurement accuracy, and reliability remains fragmented across study populations, movement tasks, and application contexts. This scoping review was designed to address two main research questions: (1) What evidence is available regarding the concurrent validity, accuracy, and reliability of OpenCap? (2) To what extent is OpenCap applicable across clinical, sports, and field-based settings? Methods: This review therefore synthesizes existing evidence on the validation and practical applicability of OpenCap. The scoping review was conducted in accordance with the PRISMA extension for scoping reviews (PRISMA-ScR). The systematic search identified 51 eligible studies, which included validation studies and applied studies using OpenCap. Results: Comparisons with reference-standard systems indicated that OpenCap performed most accurately for sagittal-plane measurements. Accuracy was generally higher for lower-extremity measurements than for upper-extremity measurements, in healthy individuals than in clinical populations, and during squatting and walking tasks than during jumping tasks. Conclusion: Future research should focus on expanding validation datasets across heterogeneous populations, improving tracking robustness under occlusion, and integrating multimodal sensing with large language model-assisted interpretation to support automated and context-aware biomechanical assessment.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.