THE EFFECTIVENESS OF DEVELOPING TECHNICAL TRAINING BASED ON ANALYTICAL ANALYSIS OF THE MOVEMENTS OF YOUNG SPRINTERS

This article explores the development of movement technique through the analytical analysis of the young distance of motion the initial segment of movement where biomechanical efficiency and neuromuscular coordination are established. Using advanced tools such as motion capture, force plates, electromyography, and machine learning, the study identifies critical variables in this early phase that influence overall performance, injury risk, and energy efficiency. The findings reveal that optimizing the young distance phase leads to improved movement precision, reduced asymmetries, and enhanced athletic outcomes. A novel training framework is proposed, combining biomechanical feedback, cognitive engagement, and individualized adjustments to maximize performance potential from the outset of movement. This research offers a foundational step in redefining sports training and rehabilitation practices through the lens of scientific precision and early-phase motion control.

Institutions

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2025-04-06
DOI
https://doi.org/10.5281/zenodo.15163788
Primary Topic
Sports Performance and Training
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

THE EFFECTIVENESS OF DEVELOPING TECHNICAL TRAINING BASED ON ANALYTICAL ANALYSIS OF THE MOVEMENTS OF YOUNG SPRINTERS

Zenodo (CERN European Organization for Nuclear Research)
Sports Performance and Training
article

THE EFFECTIVENESS OF DEVELOPING TECHNICAL TRAINING BASED ON ANALYTICAL ANALYSIS OF THE MOVEMENTS OF YOUNG SPRINTERS

article en

Abstract

This article explores the development of movement technique through the analytical analysis of the young distance of motion the initial segment of movement where biomechanical efficiency and neuromuscular coordination are established. Using advanced tools such as motion capture, force plates, electromyography, and machine learning, the study identifies critical variables in this early phase that influence overall performance, injury risk, and energy efficiency. The findings reveal that optimizing the young distance phase leads to improved movement precision, reduced asymmetries, and enhanced athletic outcomes. A novel training framework is proposed, combining biomechanical feedback, cognitive engagement, and individualized adjustments to maximize performance potential from the outset of movement. This research offers a foundational step in redefining sports training and rehabilitation practices through the lens of scientific precision and early-phase motion control.

Zenodo (CERN European Organization for Nuclear Research)
Namangan State University (UZ)
Affordable and clean energy
Openalex Percentile: Top 47%
Sports Performance and Training
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.