Aragon Institute of Engineering Research, University of Zaragoza, Mariano Esquillor, 50018 Zaragoza, Spain.
Sensors (Basel). 2019 Nov 16;19(22):5004. doi: 10.3390/s19225004.
This paper aims to design and implement a system capable of distinguishing between different activities carried out during a tennis match. The goal is to achieve the correct classification of a set of tennis strokes. The system must exhibit robustness to the variability of the height, age or sex of any subject that performs the actions. A new database is developed to meet this objective. The system is based on two sensor nodes using Bluetooth Low Energy (BLE) wireless technology to communicate with a PC that acts as a central device to collect the information received by the sensors. The data provided by these sensors are processed to calculate their spectrograms. Through the application of innovative deep learning techniques with semi-supervised training, it is possible to carry out the extraction of characteristics and the classification of activities. Preliminary results obtained with a data set of eight players, four women and four men have shown that our approach is able to address the problem of the diversity of human constitutions, weight and sex of different players, providing accuracy greater than 96.5% to recognize the tennis strokes of a new player never seen before by the system.
本文旨在设计并实现一个能够区分网球比赛中不同动作的系统。目标是实现对一组网球击球动作的正确分类。该系统必须对执行动作的任何主体的身高、年龄或性别变化具有鲁棒性。为此目的开发了一个新的数据库。该系统基于两个使用蓝牙低能耗 (BLE) 无线技术的传感器节点，与充当中央设备以收集传感器接收到的信息的 PC 进行通信。处理这些传感器提供的数据以计算它们的声谱图。通过应用具有半监督训练的创新深度学习技术，可以进行特征提取和活动分类。使用来自八名球员（四女四男）的数据集获得的初步结果表明，我们的方法能够解决不同球员的人体结构、体重和性别的多样性问题，提供超过 96.5%的准确性来识别系统以前从未见过的新球员的网球击球动作。
