ArticleSensors (Basel, Switzerland)2024
Validation of a 3D Markerless Motion Capture Tool Using Multiple Pose and Depth Estimations for Quantitative Gait Analysis.
Article in Sensors (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Dual-Model Artificial Intelligence Framework Integrating AI-Based Markerless Motion Capture for Dynamic Gait Prediction and Health-Status Classification.Diagnostics (Basel, Switzerland) · 2026Article
- 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
- Video-based gait analysis for clinical monitoring of genotype-specific functional patterns in osteogenesis imperfecta.BMC musculoskeletal disorders · 2026Article
- Three-Dimensional Kinematic Gait Analysis in Pediatric Patients With an ACL-Deficient Knee.Orthopaedic journal of sports medicine · 2026Article
- Spatiotemporal attention-based dance motion recognition and intelligent correction system using 3D motion capture.Scientific reports · 2026Article
- Evaluating a Multi-Camera Markerless System for Capturing Basketball-Specific Movements: An Exploration Using 25 Hz Video Streams.Sensors (Basel, Switzerland) · 2026Article
- Smartphone-Based Markerless Motion Capture for Spatiotemporal Gait Assessment: Applied Within-Session Reliability and Comparability of OpenCap Versus OptoGait.Sensors (Basel, Switzerland) · 2026Article
- Synchronization of OpenCap with Force Platforms: Validation of an Event-Based Algorithm.Sensors (Basel, Switzerland) · 2026Article
- Markerless 3D motion capture in return to sport decision making for ACL injured high level athletes: a narrative review with proposed clinical implementation pathway.Annals of joint · 2026Review
- Concordance of Wearable Sensors and Depth Cameras for Quantitative Gait Assessment in Multiple Sclerosis.Multiple sclerosis and related disorders · 2025Article
- Feasibility and Accuracy of an RTMPose-Based Markerless Motion Capture System for Single-Player Tasks in 3x3 Basketball.Sensors (Basel, Switzerland) · 2025Article
- Impact of Running Clothes on Accuracy of Smartphone-Based 2D Joint Kinematic Assessment During Treadmill Running Using OpenPifPaf.Sensors (Basel, Switzerland) · 2025Article
- Effects of whole-body cryostimulation on spinal and shoulder range of motion in individuals with obesity.Frontiers in rehabilitation sciences · 2025Article
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Gait analysis is essential for evaluating walking patterns and identifying functional limitations. Traditional marker-based motion capture tools are costly, time-consuming, and require skilled operators. This study evaluated a 3D Marker-less Motion Capture (3D MMC) system using pose and depth estimations with the gold-standard Motion Capture (MOCAP) system for measuring hip and knee joint angles during gait at three speeds (0.7, 1.0, 1.3 m/s). Fifteen healthy participants performed gait tasks which were captured by both systems. The 3D MMC system demonstrated good accuracy (LCC > 0.96) and excellent inter-session reliability (RMSE < 3°). However, moderate-to-high accuracy with constant biases was observed during specific gait events, due to differences in sample rates and kinematic methods. Limitations include the use of only healthy participants and limited key points in the pose estimation model. The 3D MMC system shows potential as a reliable tool for gait analysis, offering enhanced usability for clinical and research applications.
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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.