ArticleFrontiers in neurology2024
Detection of mild cognitive impairment using various types of gait tests and machine learning.
Article in Frontiers in neurology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis that pooled it.
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Who cites it
18 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Stage-Wise IoT Solutions for Alzheimer's Disease: A Systematic Review of Detection, Monitoring, and Assistive Technologies.Sensors (Basel, Switzerland) · 2025Pooled it
- Gait Analysis for Identifying Normal Cognition, Subjective Cognitive Decline, and Mild Cognitive Impairment in Parkinson Disease: Diagnostic Study.JMIR mHealth and uHealth · 2026Article
- Wearable sensing for quantifying cognitive and balance functions in naturalistic movements of older adults with mild cognitive impairment in therapeutic environments.medRxiv : the preprint server for health sciences · 2026Article
- Real-Time Foot Height Estimation and Activity Classification Using a Foot-Mounted IMU Implemented on a Smartphone.Sensors (Basel, Switzerland) · 2026Article
- Turning versus straight walking under dual-task conditions: a preliminary study for identifying mild cognitive impairment and Alzheimer's disease.BMC neurology · 2026Article
- Unlocking the silent signals: Motor kinematics as a new frontier in early detection of mild cognitive impairment.World journal of psychiatry · 2026Article
- A deep learning approach to mild cognitive impairment detection from electroencephalogram signals.Dementia & neuropsychologia · 2026Article
- Systematic review of unveiling the potential of AI using machine learning and deep learning methods in neurodegenerative diseases.PeerJ. Computer science · 2026Review
- Wearable Sensor Technologies and Gait Analysis for Early Detection of Dementia: Trends and Future Directions.Sensors (Basel, Switzerland) · 2025Review
- A comprehensive guide to digital neurology. An update on current development on digital neurology derived from the Digital Neuro Hub project.Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology · 2025Review
- Dual-task walking for early detection of Alzheimer's disease: comparative analysis of tasks using whole-body gait variables.BMC geriatrics · 2025Article
- Machine learning-based estimation of the mild cognitive impairment stage using multimodal physical and behavioral measures.Scientific reports · 2025Article
- AI-Based Severity Classification of Dementia Using Gait Analysis.Sensors (Basel, Switzerland) · 2025Article
- Comparison of Wearable and Depth-Sensing Technologies with Electronic Walkway for Comprehensive Gait Analysis.Sensors (Basel, Switzerland) · 2025Article
- Alzheimer's disease digital biomarkers multidimensional landscape and AI model scoping review.NPJ digital medicine · 2025Article
- Artificial Intelligence in the Diagnosis of Neurological Diseases Using Biomechanical and Gait Analysis Data: A Scopus-Based Bibliometric Analysis.Neurology international · 2025Review
- Objective gait analysis using Kinect v2® for the prognosis of walking during the acute phase of rehabilitation after proximal femoral fracture surgery.Frontiers in rehabilitation sciences · 2025Article
- Quad-tree Based Driver Classification using Deep Learning for Mild Cognitive Impairment Detection.IEEE access : practical innovations, open solutions · 2025Article
Corrections and comments
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Authors and funding
3 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Introduction: Alzheimer's disease and related disorders (ADRD) progressively impair cognitive function, prompting the need for early detection to mitigate its impact. Mild Cognitive Impairment (MCI) may signal an early cognitive decline due to ADRD. Thus, developing an accessible, non-invasive method for detecting MCI is vital for initiating early interventions to prevent severe cognitive deterioration. Methods: This study explores the utility of analyzing gait patterns, a fundamental aspect of human motor behavior, on straight and oval paths for diagnosing MCI. Using a Kinect v.2 camera, we recorded the movements of 25 body joints from 25 individuals with MCI and 30 healthy older adults (HC). Signal processing, descriptive statistical analysis, and machine learning techniques were employed to analyze the skeletal gait data in both walking conditions. Results and discussion: The study demonstrated that both straight and oval walking patterns provide valuable insights for MCI detection, with a notable increase in identifiable gait features in the more complex oval walking test. The Random Forest model excelled among various algorithms, achieving an 85.50% accuracy and an 83.9% F-score in detecting MCI during oval walking tests. This research introduces a cost-effective, Kinect-based method that integrates gait analysis-a key behavioral pattern-with machine learning, offering a practical tool for MCI screening in both clinical and home environments.
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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 Socratic graph.