AI-Based Detection of Tremor and Dystonia Using Deep Learning Models: A Novel Approach
Objective: Primary Objective:To develop and evaluate a machine learning-based model for the accurate classification of tremor and dystonia using video data.Secondary Objectives:To address the shortage…Integration of Multivariate Time Series Analysis in Assessing Balance Control: A Comprehensive Review of Current Research
Objective: To review the application of multivariate time series (MTS) analysis in assessing balance control and its potential for improving rehabilitation strategies. Background: Balance control…Archimedes spiral based non-linear regression machine learning model for predicting tremor’s severity
Objective: To predict tremor severity using the Archimedes spiral drawing, a commonly used item in tremor assessment scales. Background: The Archimedes spiral is widely used…A Novel Quantitative Assessment to Evaluate Functional Impairment in Parkinson’s Disease
Objective: To investigate if motion data collected from inertial measurement units (IMUs) mounted on implements used in activities of daily living (ADLs) can characterize the…Analysis of OptoGait walking data using machine learning models – Using classification algorithms to assess the risk of falling in Parkinson’s patients
Objective: The aim of the study is to determine whether the risk of falling can be detected in the gait of a patient with PD…Using R and Python for Kinematic Analysis in Robotic-Assisted Balance Training: A Review of Methodologies and Outcomes
Objective: To review the methodologies and outcomes of kinematic analysis using R and Python in robotic-assisted balance training, highlighting their role in enhancing rehabilitation strategies.…Clustering and Identification of Parkinson’s Disease Severity Subtypes Using Multimodal Data and Machine Learning Approaches
Objective: This study aimed to classify Parkinson’s disease (PD) severity subtypes by integrating objective multimodal data with machine learning (ML) techniques. We applied unsupervised clustering…Resolution Dependence of Machine Learning for Differential Diagnosis of Parkinsonian Eye Movements
Objective: To establish the extent to which high temporal resolution is required to differentiate between parkinsonian disease states from eye movements. Background: Previous studies have…Characterization of Autonomic Dysfunction Profiles in Early Parkinson’s disease
Objective: To characterize autonomic dysfunction in early Parkinson’s disease (PD) in order to improve early PD diagnosis. Background: Parkinson’s disease (PD) is a progressive neurodegenerative…Unsupervised Analysis and Clustering of 3D Parkinsonian Gait
Objective: To investigate whether deep neural networks can learn meaningful representations of gait in Parkinson’s disease (PD), and to probe whether these representations group PD…
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