ArticlePLoS computational biology2023
OpenCap: Human movement dynamics from smartphone videos.
Article in PLoS computational biology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 165 papers, 2 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
165 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Validations and applications of markerless motion capture using OpenCap: a scoping review.Frontiers in digital health · 2026Pooled it
- Explainable artificial intelligence for gait analysis: advances, pitfalls, and challenges - a systematic review.Frontiers in bioengineering and biotechnology · 2025Pooled it
- Personalised gait retraining for medial compartment knee osteoarthritis: a randomised controlled trial.The Lancet. Rheumatology · 2025Trial
- StaBLE: digital metrics capture balance performance across a wide spectrum of balance tasks and abilities.bioRxiv : the preprint server for biology · 2026Article
- Inter-Session Repeatability of a Smartphone-Based Markerless System to Measure Segment Length and Lower-Limb Kinematics in Patients With End-Stage Knee Osteoarthritis.Orthopaedic surgery · 2026Article
- Application of Deep Learning-Based Markerless Pose Estimation in a 3D Exergame System for Knee Osteoarthritis Exercise Management: Development and Preliminary Evaluation.Sensors (Basel, Switzerland) · 2026Article
- Geometric Feature Relationship-Based Knowledge Distillation for Ground Reaction Force Estimation.Measurement : journal of the International Measurement Confederation · 2026Article
- Identification of Gait Abnormalities in Patients with Hip Osteoarthritis Using a Monocular Vision 3D Motion Capture System.Bioengineering (Basel, Switzerland) · 2026Article
- Initial Validation of Markerless Gait Analysis Using SPLYZA Motion: Waveform Agreement Under Practical Measurement Conditions.Sensors (Basel, Switzerland) · 2026Article
- Agreement between markerless and marker-based motion capture for knee kinematics and kinetics during functional activities in knee osteoarthritis.Osteoarthritis and cartilage open · 2026Article
- Observational
- Co-Designing a Clinician-Centered Video Gait Assessment System for Cerebral Palsy Based on the Framework for Co-Design of Clinical Practice Tools: Iterative Development and Formative Evaluation Study.Journal of medical Internet research · 2026Article
- Dual-Model Artificial Intelligence Framework Integrating AI-Based Markerless Motion Capture for Dynamic Gait Prediction and Health-Status Classification.Diagnostics (Basel, Switzerland) · 2026Article
- Emerging Portable Technology Provides Preliminary Predictive Models of Maximum Velocity Sprinting in National Australian Track Athletes: A Foundation for Measuring Sprint Performance in the Field.Sports (Basel, Switzerland) · 2026Article
- IMU- and Vision-Based Measurement Techniques for Joint Kinematics: A Narrative Review.Sensors (Basel, Switzerland) · 2026Review
- Markerless On-Device Detection of Compensatory Movement Patterns in Upper-Limb Rehabilitation Exercises from Monocular RGB Video: A Validation Study in Healthy Adults.Sensors (Basel, Switzerland) · 2026Article
- Inverse Dynamics Meets Markerless Motion Capture during Standing: Concurrent Validation of Center of Mass and Center of Pressure Estimations.Annals of biomedical engineering · 2026Article
- EMBC Special Issue: Calibrated Uncertainty for Trustworthy Clinical Gait Analysis Using Probabilistic Multiview Markerless Motion Capture.IEEE transactions on bio-medical engineering · 2026Article
- Deep learning-based physical exercise assessment of older adults using single-camera videos.PLOS digital health · 2026Article
- GaitDynamics: a generative foundation model for analyzing human walking and running.Nature biomedical engineering · 2026Article
105 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
Abstract
Measures of human movement dynamics can predict outcomes like injury risk or musculoskeletal disease progression. However, these measures are rarely quantified in large-scale research studies or clinical practice due to the prohibitive cost, time, and expertise required. Here we present and validate OpenCap, an open-source platform for computing both the kinematics (i.e., motion) and dynamics (i.e., forces) of human movement using videos captured from two or more smartphones. OpenCap leverages pose estimation algorithms to identify body landmarks from videos; deep learning and biomechanical models to estimate three-dimensional kinematics; and physics-based simulations to estimate muscle activations and musculoskeletal dynamics. OpenCap's web application enables users to collect synchronous videos and visualize movement data that is automatically processed in the cloud, thereby eliminating the need for specialized hardware, software, and expertise. We show that OpenCap accurately predicts dynamic measures, like muscle activations, joint loads, and joint moments, which can be used to screen for disease risk, evaluate intervention efficacy, assess between-group movement differences, and inform rehabilitation decisions. Additionally, we demonstrate OpenCap's practical utility through a 100-subject field study, where a clinician using OpenCap estimated musculoskeletal dynamics 25 times faster than a laboratory-based approach at less than 1% of the cost. By democratizing access to human movement analysis, OpenCap can accelerate the incorporation of biomechanical metrics into large-scale research studies, clinical trials, and clinical practice.
Indexed as
Identifiers
What Socratic holds
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 Socratic graph.