Biomechanical and Signal-Based Characterization of Karate Lateral Kicks Using Videogrammetry Analysis

Martial arts have evolved from self-defense practices into structured competitive sports that demand high levels of neuromotor control, where improper execution remains a major source of injury. This study evaluates lower-limb control during the execution of the karate lateral kick using videogrammetry biomechanical analysis. Three participants were recorded during regular training sessions and selected according to their level of expertise. Each participant performed lateral kicks at three predefined distances (close, comfortable, and long), selected based on common training practice and individual biomechanical considerations. Videogrammetry data were generated using Kinovea version 0.9.5 software to extract sagittal ankle trajectories. Statistical analyses were carried out in MATLAB version 2025b using spatial coordinates to obtain kinematic data on the practitioner’s performance. The results revealed skill-dependent differences in movement control, characterized by temporal evolution of kinematic variables and their corresponding time–frequency representations. Novice practitioners exhibited limited control during the raising and recovery phases, despite reaching the target. In contrast, expert practitioners demonstrated consistent posture, controlled acceleration during impact, and stable limb trajectories during descent. These observations provide a foundation for data-driven classification of kick execution quality and outline potential applications in supervised learning, real-time feedback systems, and injury risk reduction during karate training.

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Publication Details

Journal
Machines
Published
2026-03-17
DOI
https://doi.org/10.3390/machines14030339
Primary Topic
Sports Performance and Training
Type
article
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article

Biomechanical and Signal-Based Characterization of Karate Lateral Kicks Using Videogrammetry Analysis

Luis Antonio Aguilar-Pérez, Luis Jiménez-Ángeles, Adolfo Angel Casarez-Duran, Jorge L. Rojas-Arce et al.
Machines
Sports Performance and Training
article

Biomechanical and Signal-Based Characterization of Karate Lateral Kicks Using Videogrammetry Analysis

Luis Antonio Aguilar-Pérez, Luis Jiménez-Ángeles, Adolfo Angel Casarez-Duran, Jorge L. Rojas-Arce, Christopher René Torres-SanMiguel, Carlos Alberto Espinoza-Garces
article en

Abstract

Martial arts have evolved from self-defense practices into structured competitive sports that demand high levels of neuromotor control, where improper execution remains a major source of injury. This study evaluates lower-limb control during the execution of the karate lateral kick using videogrammetry biomechanical analysis. Three participants were recorded during regular training sessions and selected according to their level of expertise. Each participant performed lateral kicks at three predefined distances (close, comfortable, and long), selected based on common training practice and individual biomechanical considerations. Videogrammetry data were generated using Kinovea version 0.9.5 software to extract sagittal ankle trajectories. Statistical analyses were carried out in MATLAB version 2025b using spatial coordinates to obtain kinematic data on the practitioner’s performance. The results revealed skill-dependent differences in movement control, characterized by temporal evolution of kinematic variables and their corresponding time–frequency representations. Novice practitioners exhibited limited control during the raising and recovery phases, despite reaching the target. In contrast, expert practitioners demonstrated consistent posture, controlled acceleration during impact, and stable limb trajectories during descent. These observations provide a foundation for data-driven classification of kick execution quality and outline potential applications in supervised learning, real-time feedback systems, and injury risk reduction during karate training.

MachinesVol. 14(3)
Instituto Politécnico Nacional (MX), Universidad Nacional Autónoma de México (MX)
Openalex Percentile: Top 19%
Sports Performance and Training
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