Related Experiment Video

Updated: Jul 26, 2026

Quantifying Learning in Young Infants: Tracking Leg Actions During a Discovery-learning Task
11:18

Quantifying Learning in Young Infants: Tracking Leg Actions During a Discovery-learning Task

Published on: June 1, 2015

Author Correction: Quantification of early learning and movement sub-structure predictive of motor performance

Vikram Jakkamsetti1, William Scudder2, Gauri Kathote2

  • 1Rare Brain Disorders Program, Department of Neurology, The University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd., Mail Code 8813, Dallas, TX, 75390-8813, USA. Vikram.Jakkamsetti@UTSouthwestern.edu.

Scientific Reports
|September 30, 2021
PubMed
Abstract

No abstract available in PubMed .

More Related Videos

Measurements of Motor Function and Other Clinical Outcome Parameters in Ambulant Children with Duchenne Muscular Dystrophy
09:18

Measurements of Motor Function and Other Clinical Outcome Parameters in Ambulant Children with Duchenne Muscular Dystrophy

Published on: January 12, 2019

Quantified Assessment of Infant's Gross Motor Abilities Using a Multisensor Wearable
09:24

Quantified Assessment of Infant's Gross Motor Abilities Using a Multisensor Wearable

Published on: May 17, 2024

Related Experiment Videos

Last Updated: Jul 26, 2026

Quantifying Learning in Young Infants: Tracking Leg Actions During a Discovery-learning Task
11:18

Quantifying Learning in Young Infants: Tracking Leg Actions During a Discovery-learning Task

Published on: June 1, 2015

Measurements of Motor Function and Other Clinical Outcome Parameters in Ambulant Children with Duchenne Muscular Dystrophy
09:18

Measurements of Motor Function and Other Clinical Outcome Parameters in Ambulant Children with Duchenne Muscular Dystrophy

Published on: January 12, 2019

Quantified Assessment of Infant's Gross Motor Abilities Using a Multisensor Wearable
09:24

Quantified Assessment of Infant's Gross Motor Abilities Using a Multisensor Wearable

Published on: May 17, 2024

Related Concept Videos

Hierarchy of Motor Control01:18

Hierarchy of Motor Control

The hierarchy of motor control refers to the different levels of organization and processing involved in controlling movement in the body. These levels range from higher cortical areas involved in planning and decision-making to lower spinal cord reflexes that respond automatically to external stimuli.

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

Impaired task-dependent cerebral cortex oxygenation in Glut1 deficiency.

Frontiers in neuroscience·2026

Mechanics of Long-Shank 5 mm Neural Probe Insertion into the Rat Brain: Effects of Geometry and Vibration-Assisted Insertion.

Micromachines·2026

Mpi-driven N-glycosylation orchestrates mucin O-glycosylation and intestinal homeostasis.

Nature communications·2026

PPa1 insufficiency drives lysosomal storage disease and inflammatory macrophage expansion in the bone marrow.

bioRxiv : the preprint server for biology·2026

Influence of Binarization Process on Vascular Density Metrics: A Quantitative Optical Coherence Tomography Angiography Assessment in Human and Porcine Retinas.

medRxiv : the preprint server for health sciences·2025

Structural insights into GM4951 as a lipid droplet GTPase regulating hepatic lipid metabolism.

Nature communications·2025

Optimal integration of electric vehicle charging stations and distributed generator in microgrids using bio-inspired optimization techniques.

Scientific reports·2026

Volumetric reference data of the orbit: a deep learning MRI analysis in the German national cohort.

Scientific reports·2026

Gene flow, kinship, and source population assignment in Chrysomya blow flies.

Scientific reports·2026

Crack initiation and propagation at crack tips of prefabricated flawed sandstone under uniaxial compression.

Scientific reports·2026

A novel multi-layer security framework for medical image transmission in cloud-based wireless body area networks (BAN) using XAI-guided obfuscation and WDTW encryption.

Scientific reports·2026

Predictive modelling of tetracycline removal by I-Bi/Bi2WO6/MWCNTs photocatalyst using RSM and ANN-PSO hybrid machine learning approach.

Scientific reports·2026

Evolutionary rise of a synaptic mechanism for creating and diversifying key reinforcement signals.

bioRxiv : the preprint server for biology·2026

Development and Validation of a Disability Risk Prediction Model for Older Adults Based on Machine Learning: A Multi-Algorithm Comparison with SHAP Interpretation.

Clinical interventions in aging·2026

Conditional length strategy based iterative learning control for locally lipschitz nonlinear systems with an adaptive observer.

ISA transactions·2026

Enhancing Diagnostic Performance of Screening Mammography Readers Using an Intelligent Bayesian-Driven Adaptive Training System.

Academic radiology·2026

Beyond predictive performance: Interpretability challenges and feature importance bias in XGBoost-based readmission models.

Geriatric nursing (New York, N.Y.)·2026

Reliability-Aware Cross-Modal Learning Behavior Sensing for Student Cognitive Bias Recognition and Teaching-Oriented Psychological Risk Warning.

Sensors (Basel, Switzerland)·2026
See all related articles
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies
Jove
Visualize
Contact Us