Surface electromyography (sEMG) gesture recognition algorithms based on convolutional neural network (CNN) usually convert one-dimensional sEMG to two-dimensional electromyogram (EMG) as the input of CNN.In order to solve the problems such as the lack of instantaneous samples of sEMG and the loss of local timing features caused by converting one-dimensional sEMG to two-dimensional EMG images,a processing method which fuses the multivariate empirical mode decomposition (MEMD) algorithm and the Hilbert space-filling curve is proposed to improve the accuracy of the gesture recognition algorithm.The open-source dataset NinaPro-DB1 is applied.Firstly,the sEMG is decomposed by the MEMD algorithm.Secondly,the decomposed intrinsic mode functions (IMFs) are used as the filled domain (Hilb-IMFs) of the Hilbert curve for mapping them to a two-dimensional EMG image.Finally,DenseNet is chosen as the basic network for gesture recognition.The experimental results show that the proposed method has a performance improvement of about 4% in gesture recognition accuracy compared with traditional signal dimensionalization method,which verifies the effectiveness of the method.