Towards A Minimalistic Sensor-Based Smart Soccer Net With Machine Learning

The global appeal and competitive nature of soccer have led to the application of technology to monitor and enhance player training and performance. Effective and data-driven training can reduce injury related to over-exertion and boost player performance. Shooting is a crucial skill in soccer and can significantly influence the outcome of a game. This work addresses the lack of a smart and privacy-aware device to track and analyze players’ shot localization accuracy, particularly for amateur players. We investigate a smart soccer net that can localize the point of impact of the ball from the perturbations of small inertial measuring sensors attached to the net. We train a feedforward neural network with raw data and statistical-based features, and a convolutional neural network with time- and frequency-based features from continuous wavelet transformation (CWT). Also, we investigated one, two, three, and four sensors deployed in selected configurations on the net. Four sensors deployed in a rhombus configuration and a model trained with CWT-based features achieved the best classification accuracy of $94 \%$. A cost-effective option of using three sensors in an inverted triangle configuration and the model trained with statistical-based features achieved $90 \%$ accuracy. This work is a step towards a minimalistic system that can be used as a training aid to automatically track the positions of soccer player shots in the goal during training sessions.