Validations and applications of markerless motion capture using OpenCap: a scoping review
Xiaochen Zhang, Dunkai Mao, Haonan Shang, Liuchen Ma, Juncheng Jia, Jia Yu et al. · Frontiers in Digital Health · 2026
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.