Article

Machine learning algorithms in spatiotemporal gait analysis can identify patients with Parkinson’s disease

2023-07-06

Abstract excerpt

Changes to spatiotemporal gait metrics in gait-altering conditions are characteristic of the pathology. This data can be interpreted by machine learning (ML) models which have recently emerged as an adjunct to clinical medicine. However, the literature is undecided regarding its utility in diagnosing pathological gait and is heterogeneous in its approach to applying ML techniques. This study aims to address these...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work: 7bb16764-86d3-5603-8f70-8da9785b4374
DOI: 10.1101/2023.07.03.23292200

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

Machine learning algorithms in spatiotemporal gait analysis can identify patients with Parkinson’s diseaseDOI 10.1101/2023.07.03.23292200Read

Gait Kinematics as Biomarkers of Preclinical Parkinson’s Disease: A Systematic Review of Sensor-Based StudiesSemantic proximity Read

Wearable Gait Biomarkers and Explainable AI Identify High Prodromal Burden in Parkinson’s DiseaseSemantic proximity Read

Sensor-based gait analysis in the premotor stage of LRRK2 G2019S-associated Parkinson's disease.Semantic proximity Read

Exploring spatiotemporal and kinematic gait parameters in individuals with Parkinson's disease across different motor phenotypes: A comparative study with and without dopaminergic therapy.Semantic proximity Read

Identifying Key Gait Features in Stroke Patients: A Machine Learning Approach with Supervised and Unsupervised ValidationSemantic proximity Read

Quantitative gait analysis and prediction usingartificial intelligence for patients with gait disordersSemantic proximity Read

Spatio-temporal Gait Parameters in Older Patients: Comparaison of a Hospitalized Population of Fallers and Non-fallersSemantic proximity Read

Digital biomechanical biomarkers in the clinical assessment of patients with peripheral neuropathiesSemantic proximity Read

Gait analysis reveals new outcome measures for monitoring disease progression in individuals with late-onset Pompe diseaseSemantic proximity Read

A Machine-learning-based Method to Detect Degradation of Motor Control Stability with Applications to Diagnosis of Presymptomatic Parkinson’s DiseaseSemantic proximity Read

Ǫuantitative Gait Categorization in Parkinson’s Disease with and without Freezing of GaitSemantic proximity Read

Convolutional neural network-based detection of early-stage Parkinson’s disease using the six-minute walk testSemantic proximity Read