Research on sports aided teaching and training decision system oriented to deep convolutional neural network

IF 1.5 Q2 COMPUTER SCIENCE, THEORY & METHODS
Qinyu Mei, Ming Li
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引用次数: 3

Abstract

Aiming at the construction of the decision-making system for sports-assisted teaching and training, this article first gives a deep convolutional neural network model for sports-assisted teaching and training decision-making. Subsequently, In order to meet the needs of athletes to assist in physical exercise, a squat training robot is built using a self-developed modular flexible cable drive unit, and its control system is designed to assist athletes in squatting training in sports. First, the human squat training mechanism is analyzed, and the overall structure of the robot is determined; second, the robot force servo control strategy is designed, including the flexible cable traction force planning link, the lateral force compensation link and the establishment of a single flexible cable passive force controller; In order to verify the effect of robot training, a single flexible cable force control experiment and a man-machine squat training experiment were carried out. In the single flexible cable force control experiment, the suppression effect of excess force reached more than 50%. In the squat experiment under 200 N, the standard deviation of the system loading force is 7.52 N, and the dynamic accuracy is above 90.2%. Experimental results show that the robot has a reasonable configuration, small footprint, stable control system, high loading accuracy, and can assist in squat training in physical education.
面向深度卷积神经网络的体育辅助教学训练决策系统研究
针对体育辅助教学与训练决策系统的构建,本文首先给出了体育辅助教学与训练决策的深度卷积神经网络模型。随后,为了满足运动员辅助体育锻炼的需要,采用自主研发的模块化柔性电缆驱动单元搭建了深蹲训练机器人,并设计了其控制系统,辅助运动员在运动中进行深蹲训练。首先,分析了人体深蹲训练机理,确定了机器人的整体结构;其次,设计了机器人力伺服控制策略,包括柔性索牵引力规划环节、侧向力补偿环节和建立单柔性索被动力控制器;为了验证机器人训练的效果,进行了单柔性索力控制实验和人机深蹲训练实验。在单根软索受力控制实验中,对超力的抑制效果达到50%以上。在200 N下的深蹲实验中,系统加载力的标准差为7.52 N,动态精度在90.2%以上。实验结果表明,该机器人结构合理,占地面积小,控制系统稳定,加载精度高,可以辅助体育教学中的深蹲训练。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
2.80
自引率
23.10%
发文量
31
期刊介绍: The International Journal of Fuzzy Logic and Intelligent Systems (pISSN 1598-2645, eISSN 2093-744X) is published quarterly by the Korean Institute of Intelligent Systems. The official title of the journal is International Journal of Fuzzy Logic and Intelligent Systems and the abbreviated title is Int. J. Fuzzy Log. Intell. Syst. Some, or all, of the articles in the journal are indexed in SCOPUS, Korea Citation Index (KCI), DOI/CrossrRef, DBLP, and Google Scholar. The journal was launched in 2001 and dedicated to the dissemination of well-defined theoretical and empirical studies results that have a potential impact on the realization of intelligent systems based on fuzzy logic and intelligent systems theory. Specific topics include, but are not limited to: a) computational intelligence techniques including fuzzy logic systems, neural networks and evolutionary computation; b) intelligent control, instrumentation and robotics; c) adaptive signal and multimedia processing; d) intelligent information processing including pattern recognition and information processing; e) machine learning and smart systems including data mining and intelligent service practices; f) fuzzy theory and its applications.
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