ArticleCureus2025
Gait Analysis Using an Artificial Intelligence-Based Motion Capture System With a Single Smartphone Camera.
Article in Cureus, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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
2 citing papers in PubMed.
- Assessing the Validity and Reliability of a Markerless Motion Capture System for Sagittal-Plane Gait Range of Motion.Cureus · 2025Article
- Main elements of current spine biomechanics research: model, installation and test data.Frontiers in bioengineering and biotechnology · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionMotion capture is widely used to analyze human gait and enables measurement of various biomechanical parameters. However, conventional infrared-based motion-capture systems are expensive and require a large amount of space, making them difficult to implement in many facilities. Recently, artificial intelligence (AI) has been applied in numerous medical fields, including gait analysis. This study aimed to evaluate the effectiveness of an AI-based motion capture system using a single smartphone camera compared to a conventional infrared-based motion capture system. METHODS AND ANALYSIS: Twenty-two straight walks of healthy volunteers were simultaneously captured using a smartphone (iPhone X
resultsThe shapes of the hip and knee flexion angle graphs in Group AI were similar to those in Group M. Variables, such as gait velocity, bilateral step length, and maximum flexion angle of the hip and knee joints, showed high accuracy. Most variables showed high correlation coefficients (gait velocity, r = 0.94; right and left step lengths, r = 0.91 and 0.93; right and left maximum flexion angle of the hip joint, r = 0.87 and 0.71; knee joint, r = 0.84 and 0.93; right and left minimum flexion angles of the hip joint, r = 0.73 and 0.75). However, low correlation coefficients were observed in gait cycle time (r = 0.68) and minimum knee flexion angle (right and left, r = 0.30 and 0.47).
conclusionOur findings suggest that an AI-based motion capture system using a single smartphone camera may provide reliable gait parameters for certain 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.