An End-to-End Deep Learning Pipeline for Football Activity Recognition Based on Wearable Acceleration Sensors

Action statistics in sports, such as the number of sprints and jumps, along with the details of the corresponding locomotor actions, are of high interest to coaches and players, as well as medical staff. Current video-based systems have the disadvantage that they are costly and not easily transportable to new locations. In this study, we...

Football activity recognition: A deep learning approach to football activity recognition based on Inertial Measurement Units signals

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...

Micro-Activity Recognition using Wearables for Human Augmentation

Wearable devices have paved way for several context-aware applications in the field of health-care and sports to improve the well-being of users and their performance for human augmentation. During rehabilitation patients need accurate feedback that can empower and improve the speed of recovery. On the other hand competitive athletes need a...