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

Vision

Vision

Vision is the result of light being detected and transduced into neural signals by the retina of the eye. This information is then further analyzed and interpreted by the brain. First, light enters the front of the eye and is focused by the cornea and lens onto the retina—a thin sheet of neural tissue lining the back of the eye. Because of refraction through the convex lens of the eye, images are projected onto the retina upside-down and reversed.

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

Investigating smiling asymmetries in Parkinson's disease through the whistle-smile reflex.

Journal of neural transmission (Vienna, Austria : 1996)·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

Improvement of Refractory Restless Legs Syndrome in a Parkinson's Disease Patient After Semaglutide Treatment: A Novel Clinical Observation.

Journal of movement disorders·2026

Same author

Stereotypies misdiagnosed as functional neurological disorder in late-onset Niemann-Pick C.

Parkinsonism & related disorders·2026

Same author

Orofacial Drinking Tremor: A Case Series and Literature Review.

Movement disorders clinical practice·2026

Same journal

Novel Homozygous Pathogenic Variant in DNAJC6 Causes Rapid Progressive Parkinsonism.

Movement disorders clinical practice·2026

Same journal

Surgical Intervention for Posthyperglycemic Hemiballismus: Illustrative Case and Literature Review.

Movement disorders clinical practice·2026

Same journal

Cardiac Sympathetic Neuroimaging in Autonomic Synucleinopathies: <sup>123</sup>I-Metaiodobenzylguanidine and <sup>18</sup>F-Dopamine.

Movement disorders clinical practice·2026

Same journal

Acetazolamide-Responsive Exertion-Triggered Episodic Ataxia in DHDDS-Related Congenital Disorder of Glycosylation.

Movement disorders clinical practice·2026

Same journal

Epileptic-Dyskinetic Encephalopathy in Snijders Blok-Campeau Syndrome: Expanding the Phenotypic Spectrum.

Movement disorders clinical practice·2026

Same journal

Hardware Sophistications in Subthalamic Nucleus Deep Brain Stimulation for Parkinson's Disease; Is the Juice Worth the Squeeze?

Movement disorders clinical practice·2026

See all related articles

Search research articles

Related Experiment Video

Updated: May 9, 2025

VisualEyes: A Modular Software System for Oculomotor Experimentation

VisualEyes: A Modular Software System for Oculomotor Experimentation

Published on: March 25, 2011

Computer Vision Technologies in Movement Disorders: A Systematic Review.

Pasquale Maria Pecoraro^1,2, Luca Marsili³, Alberto J Espay³

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

Movement Disorders Clinical Practice

|May 6, 2025

Summary

This summary is machine-generated.

Computer Vision (CV) offers objective, non-invasive analysis for movement disorders, achieving over 80% diagnostic accuracy. Challenges remain in standardizing video settings and software for widespread clinical use.

Keywords:

computer vision kinematic analysis machine learning movement disorders quantitative analysis

More Related Videos

Eye-Tracking Control to Assess Cognitive Functions in Patients with Amyotrophic Lateral Sclerosis

Eye-Tracking Control to Assess Cognitive Functions in Patients with Amyotrophic Lateral Sclerosis

Published on: October 13, 2016

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

Related Experiment Videos

Last Updated: May 9, 2025

VisualEyes: A Modular Software System for Oculomotor Experimentation

VisualEyes: A Modular Software System for Oculomotor Experimentation

Published on: March 25, 2011

Eye-Tracking Control to Assess Cognitive Functions in Patients with Amyotrophic Lateral Sclerosis

Eye-Tracking Control to Assess Cognitive Functions in Patients with Amyotrophic Lateral Sclerosis

Published on: October 13, 2016

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

Area of Science:

Utilizes computer vision and machine learning for objective motor analysis in neurology.
Focuses on the application of markerless automated video analysis for movement disorder assessment.

Background:

Current movement disorder evaluation relies heavily on subjective phenomenology, leading to suboptimal diagnostic accuracy.
Objective, quantitative, and non-invasive motor analysis is needed to bridge the diagnostic accuracy gap.
Markerless automated video analysis, or Computer Vision (CV), offers a promising solution for ecologically valid assessment.

Purpose of the Study:

To systematically review the application of Computer Vision (CV) in the assessment, diagnosis, and monitoring of movement disorders.
To evaluate the efficacy and challenges of CV-based approaches in clinical neurology.

Main Methods:

Systematic review conducted following PRISMA guidelines, searching Cochrane, Embase, PubMed, and Scopus databases.
Search strategy encompassed 'video analysis' or 'computer vision' combined with a comprehensive list of movement disorders and related terms.
Inclusion criteria focused on studies published between 1984 and September 2024, with additional studies included based on expert judgment.

Main Results:

Out of 1099 identified studies, 61 met inclusion criteria, plus 10 additional studies.
Parkinson's disease was the most studied disorder, with gait analysis being the most frequent motor task.
Automated video analysis demonstrated diagnostic accuracies exceeding 80% for most movement disorders, with OpenPose being a common tool. CV showed strong alignment with accelerometry and clinical assessments for tremor, dystonia, and tic detection.

Conclusions:

Computer Vision (CV) shows significant potential for non-invasive quantification of movement disorder presence and severity.
Key challenges for real-world application include heterogeneity in video settings, software usage, and the need for standardized videorecording guidelines.