Real-Time Detection of Volleyball Player Movements with YOLOv8 to Support Rehabilitation

• Maria Eduarda Caetano da Silva IFCE
• William de Araújo Dias IFCE
• Kaiuska Silva Peixoto UFC
• Pedro Olavo de Paula Lima UFC
• Antonio Augusto Teixeira Peixoto IFCE
• Auzuir Ripardo de Alexandria IFCE

Resumo

This paper presents a computer vision system based on YOLOv8 for detecting and classifying critical volleyball postures, serve, set, pass (bump), and block, alongside ball tracking in real time. Designed to support physiotherapist and coaches, the system enables objective monitoring of biomechanical correctness during rehabilitation, addressing one of the key challenges in injury recovery: ensuring accurate execution of sport-specific movements. The project builds upon previous clinical research on volleyball rehabilitation and was implemented using open source tools, leveraging Google Colab for training on a custom-labeled dataset. Results demonstrate promising detection accuracy, indicating potential for integration into physiotherapy workflows and future augmented reality(AR) rehabilitation systems.

Palavras-chave: Computer Vision, YOLOv8, Volleyball, Physiotherapy, Posture Detection, Rehabilitation, Sports Analytics

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