Integration of Biomechanics and Digital Technology: Using Kinovea for Motion Analysis and Learning for Beginner Athletes

Background: The integration of biomechanics with digital motion-analysis technologies has introduced new approaches for examining movement efficiency, kinematic characteristics, and technical patterns in walking and running activities. Kinovea, as an accessible motion-analysis software, provides both visual and quantitative feedback. However, its application in supporting technique development among beginner athletes remains insufficiently explored. Aim: This study aims to describe the use of Kinovea in biomechanics training and examine its contribution to the awareness of kinematic characteristics and movement techniques among beginner athletes. Methods: A descriptive qualitative design involved 72 beginner athletes aged 18–25 years selected through purposive sampling. Data were collected over 16 weeks through interviews, field observations, and motion video recordings analyzed using Kinovea. Kinematic data focused on joint angles, stride behavior, and movement phases during walking, running, and the flight phase. Qualitative data were analyzed using content analysis with NVivo 12, while kinematic results were interpreted descriptively to identify performance patterns and areas for technical refinement. Result: Kinematic analysis showed coordinated joint-angle patterns across all phases. Walking analysis identified arm swing angles of 50.9°–58.8° and leg separation angles of 64.3°–67.2°, indicating a stable gait rhythm. The running analysis revealed knee angles of 68.8°–69.8° and elbow angles of 87.6°–89.1°, indicating efficient propulsive mechanics. The flight phase demonstrated knee angles of 81.2°–87.8° and elbow angles of 80.4°–88.3°, suggesting effective momentum use and postural stability. These measurements supported stride-efficiency assessment and technique evaluation. Qualitative findings revealed that Kinovea enabled athletes to interpret movement phases and identify technical inefficiencies through slow-motion and frame-by-frame visualization. Conclusion: Kinovea supports basic motion analysis by providing clear kinematic information and helping beginner athletes observe and refine their movement techniques. The findings also offer practical value for coaches by enabling more precise identification of inefficient patterns and guiding targeted corrections during early-stage training.