Abstract:To solve the problems of single target generation and incomplete characterization of personalized features in the study of human personalized gait generation, this paper proposes a method of human personalized gait generation based on the conditional generative adversarial networks. Firstly, a total of 51 joint angles of the whole body are set as preprocessing targets. Secondly, according to the walking parameters such as individual parameters, walking speed, joint composition and synergy relationship, data are labelled and condition information is constructed. Then, the human gait formation process is simulated by the conditional generation confrontation networks. Finally, the personalized gait with different walking characteristics is generated by adjusting the condition information. Through experimental analysis, the correlation coefficient between the personalized gait generated by the method and the real personalized walking data is larger than 098, the average absolute deviation is less than 008 rad, the absolute deviation of the threshold is below 5%, and the gait stability criterion results are within the stability interval. Experimental results show that the method can effectively generate personalized gait corresponding to different walking characteristics. Compared with similar researches, the walking features are more comprehensive and have better integrity.