ArticleFrontiers in human neuroscience2022
Feasibility of Markerless Motion Capture for Three-Dimensional Gait Assessment in Community Settings.
Article in Frontiers in human neuroscience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.
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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
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Who cites it
27 citing papers in PubMed, 45 citations in OpenAlex.
- Advancing Neurorehabilitation and Recovery Through Human Movement Quantification via Wearable Sensing.Neurorehabilitation and neural repair · 2026Article
- Walking as a Window to the Brain: Redefining Gait in Neurology.Medical sciences (Basel, Switzerland) · 2026Review
- The Clinician's Guide to Computer Vision in the Motion Capture Galaxy.Restorative neurology and neuroscience · 2026Article
- Shared Strides: Community-based, high-throughput biomechanics data collection in knee osteoarthritis.medRxiv : the preprint server for health sciences · 2026Article
- Verification of Markerless Gait Analysis: Multi-Camera and Single-Camera Approaches in Comparison to Marker-Based Gait Analysis.Medicina (Kaunas, Lithuania) · 2026Article
- Validation of a Trained AI Imaging Model for Detecting Diabetic Foot Deformities.International wound journal · 2026Article
- A Picture is Worth a Thousand Clinical Assessments: Use of Video-Based Pose Estimation to Augment Post-Stroke Upper Limb Assessment of Bilateral Tasks.Restorative neurology and neuroscience · 2026Article
- Validation of a portable pressure tile system for standing and stepping stability assessment in young and older adults.PloS one · 2026Article
- 3D-AI mouse behavior analysis system has the capability to detect abnormalities in R6/1 model mice with Huntington's disease during the pre-symptomatic phase.Frontiers in psychiatry · 2026Article
- Gait Analysis in Neurologic Disorders: Methodology, Applications, and Clinical Considerations.Neurology · 2025Review
- Markerless Motion Capture Parameters Associated with Fall Risk or Frailty: A Scoping Review.Sensors (Basel, Switzerland) · 2025Article
- Validity of AI-Driven Markerless Motion Capture for Spatiotemporal Gait Analysis in Stroke Survivors.Sensors (Basel, Switzerland) · 2025Article
- Comparison of Marker- and Markerless-Derived Lower Body Three-Dimensional Gait Kinematics in Typically Developing Children.Sensors (Basel, Switzerland) · 2025Article
- Validation of Gait Kinematics With Ramp and Stair Ascent and Descent Revealed by Markerless Motion Capture in Simulated Living Space: Test-Retest Reliability Study.JMIR rehabilitation and assistive technologies · 2025Article
- Sensor-based motion analysis for dementia detection: a systematic review.Frontiers in digital health · 2025Review
- Article
- Observational
- A comparison of lower body gait kinematics and kinetics between Theia3D markerless and marker-based models in healthy subjects and clinical patients.Scientific reports · 2024Article
- Deep learning-based screening for locomotive syndrome using single-camera walking video: Development and validation study.PLOS digital health · 2024Article
- Advances in applying somatosensory interaction technology in geriatric care: A bibliometric analysis.International journal of nursing sciences · 2024Review
Corrections and comments
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
Abstract
Three-dimensional (3D) kinematic analysis of gait holds potential as a digital biomarker to identify neuropathologies, monitor disease progression, and provide a high-resolution outcome measure to monitor neurorehabilitation efficacy by characterizing the mechanisms underlying gait impairments. There is a need for 3D motion capture technologies accessible to community, clinical, and rehabilitation settings. Image-based markerless motion capture (MLMC) using neural network-based deep learning algorithms shows promise as an accessible technology in these settings. In this study, we assessed the feasibility of implementing 3D MLMC technology outside the traditional laboratory environment to evaluate its potential as a tool for outcomes assessment in neurorehabilitation. A sample population of 166 individuals aged 9-87 years (mean 43.7, S.D. 20.4) of varied health history were evaluated at six different locations in the community over a 3-month period. Participants walked overground at self-selected (SS) and fastest comfortable (FC) speeds. Feasibility measures considered the expansion, implementation, and practicality of this MLMC system. A subset of the sample population (46 individuals) walked over a pressure-sensitive walkway (PSW) concurrently with MLMC to assess agreement of the spatiotemporal gait parameters measured between the two systems. Twelve spatiotemporal parameters were compared using mean differences, Bland-Altman analysis, and intraclass correlation coefficients for agreement (ICC
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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.