Article

Identification of kinematic biomarkers for self-reported Parkinson's disease symptoms

2022-12-05

Abstract excerpt

Monitoring the motor fluctuations and the severity of symptoms over time in Parkinson's disease (PD) patients is crucial for quantifying the progression of the disease and the adjustment of personalized therapy. The widespread availability of wearable sensors enables remote tracking of patients and the development of digital biomarkers for motor-related symptoms derived from the kinematic data acquired from these...

Topics

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

Identifiers and source

Literature Corpus work: f8bff75f-b71d-576b-831a-eb4c26c3d1bb
DOI: 10.21203/rs.3.rs-2321844/v1

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

Identification of kinematic biomarkers for self-reported Parkinson's disease symptomsDOI 10.21203/rs.3.rs-2321844/v1Read

Six Latent Variables Underlie MDS-UPDRS Scores and Reveal a Dissociation Between Patient- and Clinician-Assessed Parkinson's Disease SymptomsSemantic proximity Read

Wearable devices can identify Parkinson’s disease up to 7 years before clinical diagnosisSemantic proximity Read

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

Predicting Parkinson’s Disease Medication Regimen Using Sensor TechnologySemantic proximity Read

A Novel Machine Learning Based Framework for Developing Composite Digital Biomarkers of Disease ProgressionSemantic proximity Read

Early Detection of Parkinson’s Disease Using a Single-Arm Wearable Sensor and Convolutional Neural NetworksSemantic proximity Read

Development of a Tremor Detection Algorithm for use in an Academic Movement Disorders CenterSemantic proximity Read

Big data in Parkinson's disease: using smartphones to remotely detect longitudinal disease phenotypes.Semantic proximity Read

Integrated Genetic, Molecular, and Wearable Sensor Biomarkers Enable Bayesian Machine Learning-Driven Precision Stratification in Parkinson’s Disease: A Comprehensive Multi-Cohort Validation StudySemantic proximity Read

Patient Questionnaires Based Parkinson’s Disease Classification Using Artificial Neural NetworkSemantic proximity Read

From Clinical Criteria to AI-Based Classification of Advanced Parkinson’s Disease: A Data-Driven Approach Using Structured PPMI DataSemantic proximity Read

Accelerometry-Derived REM Sleep Behavior Disorder Predicts Future Parkinson’s Disease in the UK BiobankSemantic proximity Read