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.

中文翻译：

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面向深度卷积神经网络的运动辅助教学与训练决策系统研究

针对体育辅助教学训练决策系统的构建，本文首先给出了一种用于体育辅助教学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求，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，其控制系统旨在辅助运动员进行运动中的深蹲训练。首先分析人体下蹲训练机理，确定机器人的整体结构；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、横向力补偿环节和单柔性索被动力控制器的建立；为验证机器人训练效果，进行了单柔性索力控制实验和人机深蹲训练实验。在单根柔性索力控制实验中，抑制多余力的效果达到50%以上。在200 N下的深蹲实验中，系统加载力的标准偏差为7.52 N，动态精度在90.2%以上。实验结果表明，该机器人配置合理，占地面积小，控制系统稳定，加载精度高，可辅助体育课中的深蹲训练。在200 N下的深蹲实验中，系统加载力的标准偏差为7.52 N，动态精度在90.2%以上。实验结果表明，该机器人配置合理，占地面积小，控制系统稳定，加载精度高，可辅助体育课中的深蹲训练。在200 N下的深蹲实验中，系统加载力的标准偏差为7.52 N，动态精度在90.2%以上。实验结果表明，该机器人配置合理，占地面积小，控制系统稳定，加载精度高，可辅助体育课中的深蹲训练。