@article{ref1,
title="Using wearable sensors and a convolutional neural network for catch detection in American football",
journal="Sensors (Basel)",
year="2020",
author="Hollaus, Bernhard and Stabinger, Sebastian and Mehrle, Andreas and Raschner, Christian",
volume="20",
number="23",
pages="e6722-e6722",
abstract="Highly efficient training is a must in professional sports. Presently, this means  doing exercises in high number and quality with some sort of data logging. In  American football many things are logged, but there is no wearable sensor that logs  a catch or a drop. Therefore, the goal of this paper was to develop and verify a  sensor that is able to do exactly that. In a first step a sensor platform was used  to gather nine degrees of freedom motion and audio data of both hands in 759  attempts to catch a pass. After preprocessing, the gathered data was used to train a  neural network to classify all attempts, resulting in a classification accuracy of  93%. Additionally, the significance of each sensor signal was analysed. It turned  out that the network relies most on acceleration and magnetometer data, neglecting  most of the audio and gyroscope data. Besides the results, the paper introduces a  new type of dataset and the possibility of autonomous training in American football  to the research community.<p /> <p>Language: en</p>",
language="en",
issn="1424-8220",
doi="10.3390/s20236722",
url="http://dx.doi.org/10.3390/s20236722"
}