SynthesisJournal of neuroengineering and rehabilitation2023
A systematic review of the applications of markerless motion capture (MMC) technology for clinical measurement in rehabilitation.
Synthesis in Journal of neuroengineering and rehabilitation, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 94 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
94 citing papers in PubMed, 2 syntheses or guidelines pooled it, 149 citations in OpenAlex.
- Perception, assessment, and coaching: a systematic review and taxonomy of computer vision-based physical rehabilitation techniques.Frontiers in rehabilitation sciences · 2026Pooled it
- AI Video Analysis in Parkinson's Disease: A Systematic Review of the Most Accurate Computer Vision Tools for Diagnosis, Symptom Monitoring, and Therapy Management.Sensors (Basel, Switzerland) · 2025Pooled it
- Smartphone-based real-time feedback to suppress trunk compensation for unsupervised upper limb rehabilitation in patients with brain disorders.Journal of neuroengineering and rehabilitation · 2026Trial
- The Reliability and Validity of Active Range of Motion Measurement for Fingers Using Three-Dimensional Motion Analysis.Sensors (Basel, Switzerland) · 2026Article
- Real-Time Prediction of Lower-Limb Joint Kinematics, Kinetics, and Ground Reaction Force Using Wearable Sensors and Machine Learning.Sensors (Basel, Switzerland) · 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
- Validation of a Deep Learning-Based Markerless Video System for Gait Speed Assessment During the 10-Meter Walk Test in Outdoor Clinical Settings.Journal of functional morphology and kinesiology · 2026Article
- Artificial Intelligence in Veterinary Neurology: Comparative Insights From Human Medicine and Cross-Species Technology Transfer.Veterinary medicine and science · 2026Review
- Evaluation Protocols and Validation for Cameras in Indoor Healthcare Monitoring.Sensors (Basel, Switzerland) · 2026Article
- 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
- Use of Computer Vision with Conventional Video Recordings of Gait in Older Adults: A Scoping Review.Healthcare (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
- Review
- Event-Driven Multimodal Sensing and Computing for Context-Aware Home Monitoring Using Stereo Vision and Dietary Event Anchoring.Sensors (Basel, Switzerland) · 2026Article
- Wearable Sensing for Clinical Physiology Monitoring: Emerging Paradigms.Physiology (Bethesda, Md.) · 2026Review
- Validation of Azure Kinect for Upper Limb Motion Analysis Under Optimal and Suboptimal Conditions.Sensors (Basel, Switzerland) · 2026Article
- Development, Reliability, and Validity Assessment of a Portable 3D Camera-Based System for Quantifying Postural Sway and Balance.Sensors (Basel, Switzerland) · 2026Article
- Markerless Motion Capture for Human Movement Estimation Using Artificial Intelligence: A Systematic Review.Pediatric reports · 2026Review
- Associations Between Hip Mobility and Pain in Chronic Low Back Pain Using IMU and Markerless Motion Capture.Sensors (Basel, Switzerland) · 2026Article
34 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
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundMarkerless motion capture (MMC) technology has been developed to avoid the need for body marker placement during motion tracking and analysis of human movement. Although researchers have long proposed the use of MMC technology in clinical measurement-identification and measurement of movement kinematics in a clinical population, its actual application is still in its preliminary stages. The benefits of MMC technology are also inconclusive with regard to its use in assessing patients' conditions. In this review we put a minor focus on the method's engineering components and sought primarily to determine the current application of MMC as a clinical measurement tool in rehabilitation.
methodsA systematic computerized literature search was conducted in PubMed, Medline, CINAHL, CENTRAL, EMBASE, and IEEE. The search keywords used in each database were "Markerless Motion Capture OR Motion Capture OR Motion Capture Technology OR Markerless Motion Capture Technology OR Computer Vision OR Video-based OR Pose Estimation AND Assessment OR Clinical Assessment OR Clinical Measurement OR Assess." Only peer-reviewed articles that applied MMC technology for clinical measurement were included. The last search took place on March 6, 2023. Details regarding the application of MMC technology for different types of patients and body parts, as well as the assessment results, were summarized.
resultsA total of 65 studies were included. The MMC systems used for measurement were most frequently used to identify symptoms or to detect differences in movement patterns between disease populations and their healthy counterparts. Patients with Parkinson's disease (PD) who demonstrated obvious and well-defined physical signs were the largest patient group to which MMC assessment had been applied. Microsoft Kinect was the most frequently used MMC system, although there was a recent trend of motion analysis using video captured with a smartphone camera.
conclusionsThis review explored the current uses of MMC technology for clinical measurement. MMC technology has the potential to be used as an assessment tool as well as to assist in the detection and identification of symptoms, which might further contribute to the use of an artificial intelligence method for early screening for diseases. Further studies are warranted to develop and integrate MMC system in a platform that can be user-friendly and accurately analyzed by clinicians to extend the use of MMC technology in the disease populations.
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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.