Department of Mechatronics, MCI, Maximilianstraße 2, 6020 Innsbruck, Austria.
Department of Sport Science, University of Innsbruck, Fürstenweg 185, 6020 Innsbruck, Austria.
Sensors (Basel). 2020 Nov 24;20(23):6722. doi: 10.3390/s20236722.
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.
在职业体育中，高效的训练是必须的。目前，这意味着要进行大量高质量的练习，并进行某种数据记录。在美国足球中，有很多东西都被记录下来，但没有可穿戴传感器可以记录接球或掉球。因此，本文的目标是开发和验证一种能够做到这一点的传感器。在第一步中，使用传感器平台收集了 759 次接球尝试中双手的九个自由度运动和音频数据。在预处理之后，所收集的数据被用于训练神经网络来对所有的尝试进行分类，从而得到了 93%的分类准确率。此外，还分析了每个传感器信号的重要性。结果表明，该网络主要依赖于加速度计和磁力计数据，而忽略了大部分的音频和陀螺仪数据。除了结果，本文还向研究界介绍了一种新的数据集类型和美国足球中自主训练的可能性。
