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This study presents the development of a sensor-based training tool designed to assist beginner table tennis players in maintaining a consistent forehand grip and swing technique. The system integrates an MPU6050 motion sensor with an ESP32 microcontroller to monitor racket angle and acceleration in real time. Players receive immediate feedback through visual (LED) and auditory (buzzer) signals, allowing for real-time posture and grip corrections. The system was tested in controlled experimental conditions, analyzing stroke performance across multiple trials. The results indicated that proper strokes exhibited well-defined acceleration and rotational motion patterns, whereas inconsistent strokes showed irregular fluctuations and instability across all axes. Comparative data analysis highlighted that stable bat angles and smooth angular velocity trends were key indicators of well-executed strokes. This system provides an objective and data-driven approach to skill development, offering a structured method for players to refine their technique. Future enhancements will focus on expanding the system’s capabilities to analyze additional stroke types and incorporating AI-driven adaptive feedback for personalized training.

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Development of a Sensor-Based Tool for Assisting Beginners in Table Tennis: A Case Study on Forehand Grip Consistency

  • 会议论文

  • 发表日期：

    2026年

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  • Syukran Hakim Norazam,
  • Muhamad Ridzuan Radin Muhamad Amin,
  • Mohd Amir Shahlan Mohd Aspar,
  • Muhammad Nur Farhan Saniman,
  • Abdul Nasir Abd Ghafar

摘要

This study presents the development of a sensor-based training tool designed to assist beginner table tennis players in maintaining a consistent forehand grip and swing technique. The system integrates an MPU6050 motion sensor with an ESP32 microcontroller to monitor racket angle and acceleration in real time. Players receive immediate feedback through visual (LED) and auditory (buzzer) signals, allowing for real-time posture and grip corrections. The system was tested in controlled experimental conditions, analyzing stroke performance across multiple trials. The results indicated that proper strokes exhibited well-defined acceleration and rotational motion patterns, whereas inconsistent strokes showed irregular fluctuations and instability across all axes. Comparative data analysis highlighted that stable bat angles and smooth angular velocity trends were key indicators of well-executed strokes. This system provides an objective and data-driven approach to skill development, offering a structured method for players to refine their technique. Future enhancements will focus on expanding the system’s capabilities to analyze additional stroke types and incorporating AI-driven adaptive feedback for personalized training.