Advanced neurological activity status of athletes based on big data technology.

Heliyon

School of Physical Education, Dalian University, Dalian, 116622, Liaoning, China.

Published: September 2024

AI Article Synopsis

  • The article discusses the limited current applications of big data technology, primarily in the medical field, and proposes a new system for analyzing athletes' neural activity based on big data principles.
  • The system includes two key components: collecting biological data using advanced equipment to ensure accuracy, and employing a big data spectral clustering algorithm to efficiently handle large datasets.
  • Results indicate that the new system outperforms traditional methods in data collection and classification accuracy, achieving 100% for ECG and 90% for EEG, while highlighting significant improvements in sports training efficiency.

Article Abstract

Currently, the application scope of big data (BD for short here) technology is relatively narrow, mostly used in the medical field, and the degree of application is relatively superficial, mostly for data statistical record analysis. Therefore, By combining the literature review, this article has decided to construct a system based on BD technology for analyzing the advanced neural activity status of athletes. The system is mainly divided into two parts, one is the biological information collection part. As an important source of system data, it is necessary to use professional equipment to collect ECG and EEG data and ensure the accuracy of the data through signal filtering, Gaussian noise elimination, salt and pepper noise, and exponential noise de-noising technology. The other is the algorithm problem of BD systems. Considering that the traditional algorithm can not deal with a large amount of data effectively, this paper chooses the BD spectral clustering algorithm based on core points as the main algorithm to cluster the data. By evaluating the efficiency of system learning, data collection and classification, system scheme construction, and error rate, this article ultimately determined the practical feasibility and effectiveness of the system. After completing the construction of the system, considering the gap between the system's performance and traditional data, this article analyzed the improvement data of various aspects of sports training. This paper compares the performance differences between the system based on BD technology and the traditional data analysis method under different indicators. In terms of data collection and classification, the accuracy of the system based on BD technology in the collection and classification of ECG and EEG data reached 100 % and 90 %, respectively, which was significantly higher than 60 % and 30 % of the traditional methods. By comparing the data from five training courses, it is found that the training efficiency of the conventional method has increased by 60 % in the first course, while the efficiency of the training method based on the BD system has increased by 85 % in the fifth course. For the activation efficiency of nerve function, the activation efficiency of brain nerve function reached 60 % and 90 % respectively in the two nerve function activation training based on the BD system, which was much higher than 30 % and 45 % of the traditional methods. Through a series of tests and comparative analysis of data, the effectiveness of the BD system is finally determined, which can achieve the goal of improving athletes' training efficiency.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11409142PMC
http://dx.doi.org/10.1016/j.heliyon.2024.e37294DOI Listing

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