Table Tennis Forehand and Backhand Stroke Recognition Based on Neural Network

In the last few years, microcontroller producers started to produce SoC boards that are not only used to collect data from implemented sensors but also can be used for small neural networks implementation. The goal of this paper is to analyses the possibility of simple neural network implementation for sports monitoring but we will try to use the state of art technologies on that field. Sport monitoring devices can be used in most sports, but in this paper, the device that can recognize forehand and backhand strokes in table tennis will be developed. This task is not so complicated for development but the focus will be on the flexibility and possibility of using this system for other sports. According to the final test results in laboratory conditions, the system that has been developed is 96% accurate in table tennis forehand and backhand stroke recognition. Finally, in our implementation trained neural network was transferred to microcontroller and this approach opens some new possibilities that can be developed in future versions.