been given to Human Activity Recognition (HAR) based on signals obtained by IMUs placed on different body parts. This thesis studies the usage of Deep Learning-based models to recognize different football activities in an accurate, robust, and fast manner. Several deep architectures were trained with data captured with IMU sensors placed on football players' bodies and their performances were compared. A combination of convolutional layers followed by recurrent (bidirectional) LSTM layers showed to achieve the best results with up to 96.71% of accuracy. When using normalized data via a calibration recording, these accuracies increased up to 98.2%. Results showed that deep learning models performed better in evaluation time and prediction accuracy than traditional machine learning algorithms. An end-to-end pipeline for football activity recognition was developed that can be extended to any other HAR task. With it, not only the training was explored, but also a sliding window evaluation procedure was proposed that can be used to efficiently analyze unseen IMU recordings and recognize the activities there present. This pipeline showed to be fast and robust especially with signals consistently calibrated and can be used to recognize movements on a real-time basis. It can be concluded that a combination of deep learning models and a sliding window evaluation procedure is suitable for fast and accurate HAR tasks and can be used as input for research on injury prevention. By training and evaluating the models with more data, ideally from uncontrolled experiments such as football matches, we expect to further improve the generalization property of the classifiers.