ArticleBioengineering (Basel, Switzerland)2023
Monocular 3D Human Pose Markerless Systems for Gait Assessment.
Article in Bioengineering (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.
What it found
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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
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.
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Who cites it
5 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Perception, assessment, and coaching: a systematic review and taxonomy of computer vision-based physical rehabilitation techniques.Frontiers in rehabilitation sciences · 2026Pooled it
- Validation of a markerless motion capture app for automated scoring of sit-to-stand, timed up and go, and short physical performance battery tests in adults with chronic disease.PLOS digital health · 2026Article
- 3D pose estimation for scalable remote gait kinematics assessment.NPJ digital medicine · 2025Article
- Comparison of lower limb kinematic and kinetic estimation during athlete jumping between markerless and marker-based motion capture systems.Scientific reports · 2025Article
- Review of models for estimating 3D human pose using deep learning.PeerJ. Computer science · 2025Review
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
Gait analysis plays an important role in the fields of healthcare and sports sciences. Conventional gait analysis relies on costly equipment such as optical motion capture cameras and wearable sensors, some of which require trained assessors for data collection and processing. With the recent developments in computer vision and deep neural networks, using monocular RGB cameras for 3D human pose estimation has shown tremendous promise as a cost-effective and efficient solution for clinical gait analysis. In this paper, a markerless human pose technique is developed using motion captured by a consumer monocular camera (800 × 600 pixels and 30 FPS) for clinical gait analysis. The experimental results have shown that the proposed post-processing algorithm significantly improved the original human pose detection model (BlazePose)'s prediction performance compared to the gold-standard gait signals by 10.7% using the MoVi dataset. In addition, the predicted
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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.