Wearable Device-Based Smart Football Athlete Health Prediction Algorithm Based on Recurrent Neural Networks

Abstract

For football players who participate in sports, the word “health” is extremely important. Athletes cannot create their own value in competitive competitions without a strong foundation. Scholars have paid a lot of attention to athlete health this year, and many analysis methods have been proposed, but there have been few studies using neural networks. As a result, this article proposes a novel wearable device-based smart football player health prediction algorithm based on recurrent neural networks. To begin, this article employs wearable sensors to collect health data from football players. The time step data are then fed into a recurrent neural network to extract deep features, followed by the health prediction results. The collected football player health dataset is used in this paper to conduct experiments. The simulation results prove the reliability and superiority of the proposed algorithm. Furthermore, the algorithm presented in this paper can serve as a foundation for the football team’s and coaches’ scientific training plans.

A Comprehensive Review of Healthcare Prediction using Data Science with Deep Learning

“…However, in the area of healthcare prediction, it has not yet been completely utilized. Six sample deep architectures-Deep Belief Network (DBN) [27], Convolutional Neural Network (CNN) [28], Recurrent Neural Network (RNN) [29], Long Short-Term Memory (LSTM) [30], Auto-encoder [31], and Sparse auto encoder [32]-were primarily the focus on the published research on DL. Based on these six exemplary deep architectures, this section aims to examine existing techniques.…”

Emerging Trends in Sports and Artificial Intelligence: A Scientometric Analysis in Citespace

“…Additionally, wearable devices are a commonly used technology in this field for data collection about health and exercise. Most of this data is further processed using a machine learning algorithm [19,20]. CiteSpace is a tool that presents the literature as a network of multiple interconnected sub-networks, called 'time slices,' each built using articles published within a year.…”

“…Additionally, wearable devices are a commonly used technology in this field for data collection about health and exercise. Most of this data is further processed using a machine learning algorithm[19,20].…”

The Role of Emergent Technologies in the Dynamic and Kinematic Assessment of Human Movement in Sport and Clinical Applications

“…For example, kinematic DAQs, relying on GNSS and IMU or Ultrawideband-based location systems (UWB-further explored in Section 6. 1.3), are widely used in team sports such as rugby or football [21,22] to measure the position and displacement of the athletes on the pitch. Dynamic parameters can also be measured (e.g., the forces exerted on the oars in rowing [23] or on the paddle in flatwater kayaking [18,24]) employing strain gauge bridges, together with kinematic ones.…”