Liu Yuanxin, Cao Shufang
Sports Department, Henan Medical College, Zhengzhou, 451191, China.
Ministry of Basic Medicine Education, Dazhou Vocational College of Chinese Medicine, Dazhou, 635000, China.
Heliyon. 2024 Jun 18;10(12):e33191. doi: 10.1016/j.heliyon.2024.e33191. eCollection 2024 Jun 30.
In modern society, people's pace of life is fast, and the pressure is enormous, leading to increasingly prominent issues such as obesity and sub-health. Traditional fitness methods cannot meet people's needs to a certain extent. Therefore, this work aims to use technology to change people's lifestyles and compensate for traditional fitness methods' shortcomings. Firstly, this work overviews neurorobotics, providing neural perception and control functions for aerobics intelligent fitness system. Secondly, the connection between big data and machine learning (ML), big data technology products, and the ML process are discussed. The Spark big data platform builds node data for calculation, and the decision tree algorithm is used for data preprocessing. These are important for future intelligent fitness analysis. This work proposes an aerobics intelligent fitness system based on neurorobotics technology and big data analysis and develops a recommendation system for the best fitness exercise. This system utilizes neural perception and control functions, combined with big data and ML technology, to solve the obesity and sub-health problems faced by people in fast-paced and high-pressure lifestyles. By harnessing the computational capabilities of the Spark big data platform and applying the decision tree algorithm for data preprocessing, the system can furnish users with personalized fitness plans and optimization recommendations. This work conducts a model performance study on 35 % aerobic fitness data on intelligent fitness Android v1.0.8 to evaluate the system's data processing ability and training effectiveness. Moreover, the aerobics intelligent fitness system models based on neurorobotics, big data, and ML are evaluated. The results indicate that normalizing the data using the Min-Max method leads to a decrease in the F1 value and a reduction in data set errors. Consequently, the dataset studied by the system model is beneficial to improving the work efficiency of the aerobics intelligent fitness system. After the comprehensive human quality of the system model is evaluated, the actual average score of the comprehensive human quality of the 13 users tested before the aerobics intelligent fitness system test is 91.44, and the average prediction score is 90.88. The results of the two tests are similar. Thus, using the intelligent fitness system can enable the user to obtain system feedback according to the actual training effect, thereby playing a guiding role in the intelligent fitness of aerobics for the user. This work designs and implements the aerobics intelligent fitness system close to the human body's training effect, further enhancing the specialization and individualization of sports and fitness.
在现代社会，人们生活节奏快，压力巨大，导致肥胖和亚健康等问题日益突出。传统健身方法在一定程度上无法满足人们的需求。因此，这项工作旨在利用技术改变人们的生活方式，弥补传统健身方法的不足。首先，这项工作概述了神经机器人技术，为有氧运动智能健身系统提供神经感知和控制功能。其次，讨论了大数据与机器学习（ML）之间的联系、大数据技术产品以及ML过程。Spark大数据平台构建节点数据进行计算，并使用决策树算法进行数据预处理。这些对于未来的智能健身分析很重要。这项工作提出了一种基于神经机器人技术和大数据分析的有氧运动智能健身系统，并开发了最佳健身运动推荐系统。该系统利用神经感知和控制功能，结合大数据和ML技术，解决人们在快节奏、高压力生活方式下面临的肥胖和亚健康问题。通过利用Spark大数据平台的计算能力并应用决策树算法进行数据预处理，该系统可以为用户提供个性化的健身计划和优化建议。这项工作对智能健身安卓v1.0.8上35%的有氧健身数据进行了模型性能研究，以评估系统的数据处理能力和训练效果。此外，还对基于神经机器人技术、大数据和ML的有氧运动智能健身系统模型进行了评估。结果表明，使用最小-最大方法对数据进行归一化会导致F1值下降和数据集误差减少。因此，系统模型所研究的数据集有利于提高有氧运动智能健身系统的工作效率。在对系统模型的综合人类素质进行评估后，在有氧运动智能健身系统测试前对13名测试用户的综合人类素质实际平均得分为91.44，平均预测得分为90.88。两次测试结果相似。因此，使用智能健身系统可以让用户根据实际训练效果获得系统反馈，从而对用户的有氧运动智能健身起到指导作用。这项工作设计并实现了接近人体训练效果的有氧运动智能健身系统，进一步提高了运动健身的专业化和个性化程度。
