ArticleSensors (Basel, Switzerland)2023
Identification of a Gait Pattern for Detecting Mild Cognitive Impairment in Parkinson's Disease.
Article in Sensors (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 5 of them syntheses that pooled it.
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Who cites it
15 citing papers in PubMed, 5 syntheses or guidelines pooled it.
- Advances in gait research related to Alzheimer's disease.Frontiers in neurology · 2025Pooled it
- Machine learning methods for the detection and prediction of cognitive impairment in Parkinson's disease: a systematic review and meta-analysis.Frontiers in aging neuroscience · 2025Pooled it
- The Role of Deep Learning and Gait Analysis in Parkinson's Disease: A Systematic Review.Sensors (Basel, Switzerland) · 2024Pooled it
- Machine learning for the detection and diagnosis of cognitive impairment in Parkinson's Disease: A systematic review.PloS one · 2024Pooled it
- Machine learning based algorithms for virtual early detection and screening of neurodegenerative and neurocognitive disorders: a systematic-review.Frontiers in neurology · 2024Pooled it
- Gait Analysis for Identifying Normal Cognition, Subjective Cognitive Decline, and Mild Cognitive Impairment in Parkinson Disease: Diagnostic Study.JMIR mHealth and uHealth · 2026Article
- Using explainable AI to identify disease-relevant and deep brain stimulation treatment-sensitive gait features in Parkinson's disease.Journal of neuroengineering and rehabilitation · 2026Article
- Fist-Palm Test May Identify Mild Cognitive Impairment and Freezing of Gait in Parkinson's Disease: A Machine Learning Approach.Movement disorders clinical practice · 2025Article
- Dual-task-related gait patterns as possible marker of precocious and subclinical cognitive alterations in Parkinson disease.Scientific reports · 2025Article
- Biomechanics Parameters of Gait Analysis to Characterize Parkinson's Disease: A Scoping Review.Sensors (Basel, Switzerland) · 2025Article
- Diagnostic classification of mild cognitive impairment in Parkinson's disease using subject-level stratified machine-learning analysis.Frontiers in aging neuroscience · 2025Article
- A kinematic dataset of locomotion with gait and sit-to-stand movements of young adults.Scientific data · 2024Article
- Mild cognitive impairment in Parkinson's disease: current view.Frontiers in cognition · 2024Review
- Detection of mild cognitive impairment using various types of gait tests and machine learning.Frontiers in neurology · 2024Article
- ETMT: A Tool for Eye-Tracking-Based Trail-Making Test to Detect Cognitive Impairment.Sensors (Basel, Switzerland) · 2023Article
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Authors and funding
7 authors.
Funding
Abstract
The aim of this study was to determine a gait pattern, i.e., a subset of spatial and temporal parameters, through a supervised machine learning (ML) approach, which could be used to reliably distinguish Parkinson's Disease (PD) patients with and without mild cognitive impairment (MCI). Thus, 80 PD patients underwent gait analysis and spatial-temporal parameters were acquired in three different conditions (normal gait, motor dual task and cognitive dual task). Statistical analysis was performed to investigate the data and, then, five ML algorithms and the wrapper method were implemented: Decision Tree (DT), Random Forest (RF), Naïve Bayes (NB), Support Vector Machine (SVM) and K-Nearest Neighbour (KNN). First, the algorithms for classifying PD patients with MCI were trained and validated on an internal dataset (sixty patients) and, then, the performance was tested by using an external dataset (twenty patients). Specificity, sensitivity, precision, accuracy and area under the receiver operating characteristic curve were calculated. SVM and RF showed the best performance and detected MCI with an accuracy of over 80.0%. The key features emerging from this study are stance phase, mean velocity, step length and cycle length; moreover, the major number of features selected by the wrapper belonged to the cognitive dual task, thus, supporting the close relationship between gait dysfunction and MCI in PD.
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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.