The advent of machine learning in recent years has given a hope for modeling in the field of human physical rehabilitation exercises, and the classification recognition based on deep learning has achieved a high recognition rate. The characteristics of the depth model can make the sensor suffer from noise attacks in the recognition rate. Thus here based on the Wasserstein generative adversarial network (WGAN), the generative physical rehabilitation exercise GAN (GPREGAN) framework is proposed, which is improved to disguise aggressive data as normal data. This adversarial data is so highly similar to the original data that the detection algorithms cannot distinguish between them. The generated adversarial data is fed into a deep recognition model based on convolutional neural network (CNN) and long short-term memory (LSTM) network in the experiments, and the detection rate is reduced from 99% to 0 by successfully attacking the network. To evaluate the effectiveness of the generated adversarial samples, the paper uses the sample mean square error for evaluation. It is demonstrated that the GPREGAN framework has the ability to generate time-series data analogous to that in the field of human physical rehabilitation exercises and to increase the diversity of samples in this field.