Enhancing Athletic Performance Through Wearable Technology Integration in Volleyball: A Pilot Study

Keywords:
Kinexon, Accumulated Acceleration Load, Accumulated Acceleration Load per minute, Volleyball

Abstract:
This article investigates the technical and practical aspects of using Kinexon IMU technology and acceleration load evaluation in volleyball monitoring to improve athletic performance and training prescription. The focus is on integrating these technologies to maximise training efficiency and improve player performance. The research highlights the importance of wearable sensors and acceleration metrics in capturing real-time player movement data, which can enhance training strategies and reduce injury risks. By utilising acceleration-based workload monitoring, coaches and sports scientists can personalise training programs, manage workload effectively, and prepare athletes for varying intensity and volume scenarios. The study measures physical demands and workload distribution using metrics such as Accumulated Acceleration Load and Accumulated Acceleration Load per minute. These metrics are beneficial for optimising athlete development and performance in elite volleyball teams at the national level. The findings underscore the transformative impact of using Kinexon IMU technology in external volleyball monitoring, providing valuable insights for structuring training sessions, managing training intensity, and optimising performance outcomes

CITATION:

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V. Banković, A. Živković, N. Trunić, “Enhancing Athletic Performance Through Wearable Technology Integration in Volleyball: A Pilot Study,” in Sinteza 2024 - International Scientific Conference on Information Technology, Computer Science, and Data Science, Belgrade, Singidunum University, Serbia, 2024, pp. 358-363. doi:10.15308/Sinteza-2024-358-363

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Banković, V., Živković, A., Trunić, N. (2024). Enhancing Athletic Performance Through Wearable Technology Integration in Volleyball: A Pilot Study. Paper presented at Sinteza 2024 - International Scientific Conference on Information Technology, Computer Science, and Data Science. doi:10.15308/Sinteza-2024-358-363

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