Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Parkinson's Disease: Overview

Parkinson's Disease: Overview

Neurodegenerative disorders are progressive diseases that cause irreversible damage and loss to neurons in specific brain areas. Examples of these disorders include Parkinson's disease, Alzheimer's disease, Multiple Sclerosis (MS), and Amyotrophic Lateral Sclerosis (ALS). These disorders share characteristics such as proteinopathies, selective neuronal vulnerability, and a complex interplay between genetic and environmental factors. The primary therapeutic goal for these conditions is...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Bridging the global Parkinson's divide: Technology as a structural solution for equitable and brain health-integrated care.

Journal of Parkinson's disease·2026

Same author

Driving Cerebellar Theta Oscillations Interferes With Voluntary Neck Movements in Cervical Dystonia.

Movement disorders : official journal of the Movement Disorder Society·2026

Same author

Beyond hypoxia: Systemic acidemia and respiratory insufficiency in Lance-Adams syndrome.

Journal of the neurological sciences·2026

Same author

Driving motor cortex oscillations restores plasticity and improves bradykinesia features in Parkinson's disease.

NPJ Parkinson's disease·2026

Same author

Orofacial Drinking Tremor: A Case Series and Literature Review.

Movement disorders clinical practice·2026

Same author

Systematic review of movement disorders mislabeled as functional: when incongruence misleads.

Journal of neurology·2026

Search research articles

Related Experiment Video

Updated: Jan 8, 2026

Characterizing the Relationship Between Eye Movement Parameters and Cognitive Functions in Non-demented Parkinson's Disease Patients with Eye Tracking

Characterizing the Relationship Between Eye Movement Parameters and Cognitive Functions in Non-demented Parkinson's Disease Patients with Eye Tracking

Published on: September 26, 2019

Computer Vision Analysis for Objective Motor Assessment in Parkinson's Disease: A Retrospective Study.

Pasquale Maria Pecoraro^1,2, Luca Marsili³, Antonio Cannavacciuolo⁴

¹Operative Research Unit of Neurology, Fondazione Policlinico Universitario Campus Bio-Medico, Rome, Italy.

Movement Disorders Clinical Practice

|December 20, 2025

Summary

This summary is machine-generated.

Computer vision (CV) analysis of finger tapping objectively distinguishes Parkinson's disease (PD) from healthy individuals. CV features also correlate with clinical severity, offering a potential tool for motor assessment in PD patients.

Keywords:

bradykinesia computer vision digital biomarkers machine learning quantitative analysis telemedicine

More Related Videos

Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease

Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease

Published on: July 24, 2019

Driving Simulation in the Clinic: Testing Visual Exploratory Behavior in Daily Life Activities in Patients with Visual Field Defects

Driving Simulation in the Clinic: Testing Visual Exploratory Behavior in Daily Life Activities in Patients with Visual Field Defects

Published on: September 18, 2012

Related Experiment Videos

Last Updated: Jan 8, 2026

Characterizing the Relationship Between Eye Movement Parameters and Cognitive Functions in Non-demented Parkinson's Disease Patients with Eye Tracking

Characterizing the Relationship Between Eye Movement Parameters and Cognitive Functions in Non-demented Parkinson's Disease Patients with Eye Tracking

Published on: September 26, 2019

Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease

Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease

Published on: July 24, 2019

Driving Simulation in the Clinic: Testing Visual Exploratory Behavior in Daily Life Activities in Patients with Visual Field Defects

Driving Simulation in the Clinic: Testing Visual Exploratory Behavior in Daily Life Activities in Patients with Visual Field Defects

Published on: September 18, 2012

Area of Science:

Neurology
Biomedical Engineering
Medical Imaging

Background:

The Movement Disorder Society-Unified Parkinson's Disease Rating Scale-Part III (MDS-UPDRS-III) is subjective and lacks sensitivity for early Parkinson's disease (PD) detection.
Computer vision (CV) offers a method for objective kinematic analysis from routine videos, potentially improving PD motor assessment.

Purpose of the Study:

To identify CV-derived finger-tapping features that differentiate PD patients from healthy controls (HC).
To quantify the relationship between these CV features and clinical measures, including MDS-UPDRS-III and DAT-SPECT.

Main Methods:

Retrospective analysis of finger-tapping videos from PD patients and HC.
Utilized a Mediapipe-based pipeline to quantify tapping velocity, amplitude changes, and amplitude/rhythm variability.
Assessed diagnostic performance using ROC AUC and correlations with clinical scores via Spearman analysis.

Main Results:

Amplitude and rhythm variability demonstrated high discriminatory capacity between PD and HC (AUCs > 0.83).
CV-derived amplitude variability and decrement correlated with MDS-UPDRS-III scores and finger-tapping severity (item 3.4).
Tapping velocity negatively correlated with MDS-UPDRS-III, while amplitude variability correlated with disease duration.

Conclusions:

CV-based analysis of finger-tapping provides objective kinematic measures.
These objective measures effectively discriminate PD from HC and correlate with clinical motor severity and disease progression.