Arduino Nano-Based System for Tennis Shot Classification

Wearable technology has gained significant attention in research and commercial applications, including sports, where data collection and analysis play a crucial role in improving skills. This study focuses on tennis and the real-time classification of main shots, such as forehand, backhand, and serve. While previous studies have utilized machine learning methods for classification, they often relied on cloud or desktop processing. This paper proposes a novel neural architecture for real-time shot classification using an embedded device directly attached to a tennis racket, specifically the Arduino Nano 33 BLE Sense. The system processes six-axis time-series data collected from the IMU sensor, and the goal is to develop a lightweight model that can operate within the computational and memory limitations of edge devices. A 1-D convolutional neural network (CNN) is proposed for shot classification, which can effectively process 1-D time series. The experimental results demonstrate the successful classification of forehand, backhand, and serve shots using the trained model. This work highlights the potential of time series analysis in sports activities and emphasizes the importance of leveraging low-power embedded devices for efficient real-time analysis in the field.