ArticleScientific reports2023
Quantitative gait analysis and prediction using artificial intelligence for patients with gait disorders.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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
9 citing papers in PubMed, 19 citations in OpenAlex.
- Artificial intelligence for gait and balance in neurological disorders: a scoping review of clinical applications and technologies.Journal of neurology · 2026Article
- Stance and Gait Analysis of Apollo Astronauts on the Moon.Muscle & nerve · 2026Article
- Short-Term Impact of Dry Needling Treatment for Myofascial Pain on Equine Biomechanics Through Artificial Intelligence-Based Gait Analysis.Animals : an open access journal from MDPI · 2025Article
- Article
- Early adherence to biofeedback training predicts long-term improvement in stroke patients: A machine learning approach.PloS one · 2025Article
- A validation study demonstrating portable motion capture cameras accurately characterize gait metrics when compared to a pressure-sensitive walkway.Scientific reports · 2024Article
- What does artificial intelligence mean in rheumatology?Archives of rheumatology · 2024Review
- Relationship between Body Composition and Gait Characteristics in Patients with Cerebral Small Vessel Disease.Current neurovascular research · 2024Article
- Interpretable machine learning comprehensive human gait deterioration analysis.Frontiers in neuroinformatics · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors at 5 institutions in 1 country.
Funding
Abstract
Quantitative Gait Analysis (QGA) is considered as an objective measure of gait performance. In this study, we aim at designing an artificial intelligence that can efficiently predict the progression of gait quality using kinematic data obtained from QGA. For this purpose, a gait database collected from 734 patients with gait disorders is used. As the patient walks, kinematic data is collected during the gait session. This data is processed to generate the Gait Profile Score (GPS) for each gait cycle. Tracking potential GPS variations enables detecting changes in gait quality. In this regard, our work is driven by predicting such future variations. Two approaches were considered: signal-based and image-based. The signal-based one uses raw gait cycles, while the image-based one employs a two-dimensional Fast Fourier Transform (2D FFT) representation of gait cycles. Several architectures were developed, and the obtained Area Under the Curve (AUC) was above 0.72 for both approaches. To the best of our knowledge, our study is the first to apply neural networks for gait prediction tasks.
Indexed as
Identifiers
What OpenQuestion holds
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.