ArticleScientific reports2020
The detection of age groups by dynamic gait outcomes using machine learning approaches.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers, 1 of them a synthesis that pooled it.
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Who cites it
29 citing papers in PubMed, 1 synthesis or guideline pooled it, 81 citations in OpenAlex.
- Systematic Review on the Applicability of Principal Component Analysis for the Study of Movement in the Older Adult Population.Sensors (Basel, Switzerland) · 2022Pooled it
- Meta-heuristic and machine learning based functional capacity prediction using gait parameters in patients with heart failure.Scientific reports · 2026Article
- Individual Variability in Deep Learning-Based Joint Angle Estimation from a Single IMU: A Cross-Population Study.Sensors (Basel, Switzerland) · 2025Article
- Article
- Assessment of Postural Stability in Semi-Open Prisoners: A Pilot Study.Journal of clinical medicine · 2025Article
- The potential of machine learning in classifying relapse and non-relapse in children with clubfoot based on movement patterns.Scientific reports · 2025Article
- Development of an IMU-Based Post-Stroke Gait Data Acquisition and Analysis System for the Gait Assessment and Intervention Tool.Sensors (Basel, Switzerland) · 2025Article
- Early detection of autism spectrum disorder: gait deviations and machine learning.Scientific reports · 2025Article
- Prediction of future aging-related slow gait and its determinants with deep learning and logistic regression.PloS one · 2025Article
- Steps to Facilitate the Use of Clinical Gait Analysis in Stroke Patients: The Validation of a Single 2D RGB Smartphone Video-Based System for Gait Analysis.Sensors (Basel, Switzerland) · 2024Article
- Leveraging feature selection for enhanced fall risk prediction in elderly using gait analysis.Medical & biological engineering & computing · 2024Article
- A validation study demonstrating portable motion capture cameras accurately characterize gait metrics when compared to a pressure-sensitive walkway.Scientific reports · 2024Article
- Association between gait video information and general cardiovascular diseases: a prospective cross-sectional study.European heart journal. Digital health · 2024Article
- Walking on a Balance Beam as a New Measure of Dynamic Balance to Predict Falls in Older Adults and Patients with Neurological Conditions.Sports medicine - open · 2024Article
- Adaptive Control Method for Gait Detection and Classification Devices with Inertial Measurement Unit.Sensors (Basel, Switzerland) · 2023Article
- Computer Vision and Machine Learning-Based Gait Pattern Recognition for Flat Fall Prediction.Sensors (Basel, Switzerland) · 2022Article
- Random forest algorithms to classify frailty and falling history in seniors using plantar pressure measurement insoles: a large-scale feasibility study.BMC geriatrics · 2022Article
- GaitRec-Net: A Deep Neural Network for Gait Disorder Detection Using Ground Reaction Force.PPAR research · 2022Article
- Stable Sparse Classifiers predict cognitive impairment from gait patterns.Frontiers in psychology · 2022Article
- Gait analysis under the lens of statistical physics.Computational and structural biotechnology journal · 2022Review
Corrections and comments
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Authors and funding
7 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Prevalence of gait impairments increases with age and is associated with mobility decline, fall risk and loss of independence. For geriatric patients, the risk of having gait disorders is even higher. Consequently, gait assessment in the clinics has become increasingly important. The purpose of the present study was to classify healthy young-middle aged, older adults and geriatric patients based on dynamic gait outcomes. Classification performance of three supervised machine learning methods was compared. From trunk 3D-accelerations of 239 subjects obtained during walking, 23 dynamic gait outcomes were calculated. Kernel Principal Component Analysis (KPCA) was applied for dimensionality reduction of the data for Support Vector Machine (SVM) classification. Random Forest (RF) and Artificial Neural Network (ANN) were applied to the 23 gait outcomes without prior data reduction. Classification accuracy of SVM was 89%, RF accuracy was 73%, and ANN accuracy was 90%. Gait outcomes that significantly contributed to classification included: Root Mean Square (Anterior-Posterior, Vertical), Cross Entropy (Medio-Lateral, Vertical), Lyapunov Exponent (Vertical), step regularity (Vertical) and gait speed. ANN is preferable due to the automated data reduction and significant gait outcome identification. For clinicians, these gait outcomes could be used for diagnosing subjects with mobility disabilities, fall risk and to monitor interventions.
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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.