Smartphone-Based Gait Analysis with OpenCap: A Narrative Review

Background: Gait analysis plays a key role in detecting and monitoring neurological, musculoskeletal, and orthopedic impairments. While marker-based motion capture (MoCap) systems are the gold standard, their cost and complexity limit routine use. Recent advances in computer vision have enabled markerless smartphone-based approaches. OpenCap, an open-source platform for 3D motion analysis, offers a potentially accessible alternative. This review summarizes current evidence on its accuracy, limitations, and clinical applicability in gait assessment. Methods: A search was performed in major scientific databases to identify studies published from OpenCap’s release in 2023 to June 2025. Articles were included if they applied OpenCap to human gait and reported quantitative biomechanical outcomes. Both validation and applied studies were considered, and findings were synthesized qualitatively. Results: Nine studies were included. Validation research showed OpenCap achieved generally acceptable accuracy kinematics (RMSE 4–6°) in healthy gait, while increased errors were reported for pathological gait patterns. Applied studies confirmed feasibility in different clinical conditions, though trial-to-trial variability remained higher than MoCap, and test–retest reliability was moderate, with minimal detectable changes often exceeding 5°, limiting sensitivity to subtle clinical differences. Conclusions: OpenCap is a promising, low-cost tool for gait screening, remote monitoring, and tele-rehabilitation. Its strengths lie in accessibility and feasibility outside laboratory settings, but limitations in multiplanar accuracy, pathological gait assessment, and kinetic estimation currently preclude its replacement of MoCap in advanced clinical applications. Further research should refine algorithms and standardize protocols to improve robustness and clinical utility.