Neural Architecture for Tennis Shot Classification on Embedded System

Data analysis has become a common practice in professional and amateur sport activities, to monitor the player state and enhance performance. In tennis, performance analysis requires detecting and recognizing the different types of shots. With the advances in microcontrollers and machine learning algorithms, this topic becomes ever more considerable. We propose a 1-D convolutional neural network (CNN) model and an embedded system based on Arduino-Nano system for real-time shot classification. The network is trained through a dataset composed of three different tennis shot types, with 6 features recorded by an inertial device placed on the racket. Results demonstrate that the proposed model is able to discriminate the tennis shots with high accuracy, also generalizing to different users. The network has been deployed on a low-cost Arduino nano 33 IoT model, with an inference time of 65 ms.