USING VIDEO ANALYSIS AND MOBILE APPLICATIONS TO CORRECT RUNNING AND JUMPING TECHNIQUE

Abstract

This article examines how video analysis and mobile applications can be integrated into the teaching and coaching of running and jumping technique in sports-university settings, with a focus on practical implementation conditions in higher education. The central premise is that technical mastery in sprinting and jumping depends not only on repeated practice, but on timely, precise feedback that converts movement outcomes into actionable corrections. Video-based feedback, when paired with sensor-enabled or coaching-oriented mobile applications, allows athletes and instructors to capture key kinematic moments, visualize errors, quantify technique parameters, and track progress across microcycles. The paper frames these tools as a didactic system rather than isolated gadgets: effective use requires clear technical models, standardized filming protocols, reliable measurement indicators, and pedagogically sound feedback routines. The study proposes an applied framework for sports universities: (1) selecting technique checkpoints for running (start, acceleration, upright mechanics, contact timing) and for jumps (approach rhythm, takeoff preparation, takeoff impulse direction, flight posture, landing), (2) organizing capture conditions (camera placement, frame rate, lighting, reference lines), (3) implementing app-supported annotation and metric extraction (angles, step frequency, contact symmetry proxies, approach velocity estimates), and (4) delivering correction through brief, individualized “micro-feedback” cycles aligned with training goals. Special attention is paid to the constraints typical for university programs:large groups, limited equipment, uneven athlete preparedness, and the need to balance educational outcomes with performance development. The article also outlines a governance layer addressing data privacy, ethical recording practices, and the risk of over-reliance on visual information at the expense of kinesthetic awareness. Expected results include improved error detection, faster stabilization of technical patterns, more consistent coaching language, and higher student engagement through self-analysis and objective progress visualization. The contribution of the paper is a structured, implementable model that links biomechanics-informed technique criteria to mobile-friendly workflows, enabling instructors to enhance the quality of technical training in running and jumping while maintaining feasibility in real university contexts.