###### Abstract  
Since continuous motion control can provide a more natural, fast and accurate man-machine interface than that of discrete motion control, it has been widely used in human-robot cooperation (HRC). Among various biological signals, the surface electromyogram (sEMG)--the signal of actions potential superimposed on the surface of the skin containing the temporal and spatial information--is one of the best signals with which to extract human motion intentions. However, most of the current sEMG control methods can only perform discrete motion estimation, and thus fail to meet the requirements of continuous motion estimation. In this paper, we propose a novel method that applies a temporal convolutional network (TCN) to sEMG-based continuous estimation. After analyzing the relationship between the convolutional kernel's size and the lengths of atomic segments (defined in this paper), we propose a large-scale temporal convolutional network (LS-TCN) to overcome the TCN's problem: that it is difficult to fully extract the sEMG's temporal features. When applying our proposed LS-TCN with a convolutional kernel size of \(1\times 31\) to continuously estimate the angles of the 10 main joints of fingers (based on the public dataset Ninapro), it can achieve a precision rate of 71.6%. Compared with TCN (kernel size of \(1\times 3\)), LS-TCN (kernel size of \(1\times 31\)) improves the precision rate by 6.6%. temporal convolutional network; human-robot cooperation; surface electromyogram; continuous motion estimation 29 March 2021 18 May 2  
equipment is often inconvenient to carry around [5]. On the other hand, physiological signals--such as sEMG--have characteristics that directly reflect human intentions, and these physiological signals are easy to collect [6]. The surface electromyogram (sEMG) is generated by action neurons in muscle. It is the signal of an action potential superimposed on the surface of the skin through time and space. The sEMG contains rich information of motor intentions [7], and can be collected in a non-invasive way. In addition, since the action potential is generated before the muscle's movement, the external information transmission can be completed 30 ms to 150 ms ahead of the actual action. The human hand--the most frequently used body part for external interactions and one of the most complex organs--can provide abundant interactive signals for HRC [8]. Compared with hands' other movements, finger movements are more delicate and complex, involving many small deep muscles and more than 20 joint degrees of freedom [9]. Hence, it is still challenging to estimate finger movement. At present, there are two methods for extracting the motion intentions of sEMG signals. One is to use a classification algorithm to classify the sEMG to generate discrete motion information, which can be used as switch signals in HRC [10; 11; 12]. However, this simple classification method cannot meet the requirements of HRC for our daily use. The other one--which is known as sEMG-based continuous estimation--is to use nonlinear models to extract continuous motion intention information (such as the angle of motion joint at each moment), which is more natural and more accurate than the classification algorithm-based method [13; 14; 1]. Hence, in the rest of this paper, we focus on the research of sEMG-based continuous estimation. Traditionally, most of motion intention estimation methods have adopted conventional machine learning algorithms to decode EMG/sEMG signals and perform artificial feature selection. [PERSON] et al. proposed a synchronous proportional multi-degree of freedom (DOF) EMG control method based on sparse constrained non-negative matrix factorization [15]. This method further expands the researchers' thinking in pattern recognition, but still cannot meet the actual needs in terms of the number of DOFs and the complexity of recognizable gestures. [PERSON] et al. used a Gaussian process to estimate hand motion [16]. The Gaussian process defines the prior function.