The use of artificial intelligence technology to analyze human behavior is one of the key research topics in the world. In order to detect and analyze the characteristics of human body behavior after training, a detection model combined with a convolutional neural network (CNN) is proposed. Firstly, the human skeleton suggestion model is established to analyze the driving mode of the human body in motion. Secondly, the number of layers and neurons in CNN are set according to the skeleton feature map. Then, the output information is classified according to the fatigue degree according to the body state after exercise. Finally, the training and performance test of the model are carried out, and the effect of the body behavior feature detection model in use is analyzed. The results show that the CNN designed in the study shows high accuracy and low loss rate in training and testing and also has high accuracy in the practical application of fatigue degree recognition after human training. According to the subjective evaluation of volunteers, the overall average evaluation is more than 9 points. The above results show that the designed convolution neural network-based detection model of body behavior characteristics after training has good performance and is feasible and practical, which has guiding significance for the design of sports training and training schemes.

中文翻译：

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基于卷积神经网络的运动训练后身体行为特征分析


利用人工智能技术分析人类行为是国际上重点研究课题之一。为了检测和分析训练后的人体行为特征，提出了一种结合卷积神经网络（CNN）的检测模型。首先，建立人体骨骼建议模型，分析人体运动中的驾驶方式。其次，根据骨架特征图设置CNN的层数和神经元数量。然后，根据运动后的身体状态，根据疲劳程度对输出信息进行分类。最后对模型进行训练和性能测试，分析身体行为特征检测模型在使用中的效果。结果表明，本研究设计的CNN在训练和测试中表现出较高的准确率和较低的丢失率，并且在人体训练后的疲劳程度识别的实际应用中也具有较高的准确率。根据志愿者的主观评价，总体平均评价在9分以上。以上结果表明，所设计的基于卷积神经网络的训练后身体行为特征检测模型性能良好，可行实用，对于运动训练和训练方案的设计具有指导意义。