# Surface Electromyography-Based Action Recognition and Manipulator Control
## 1 Introduction  
Disabled people account for a large population nowadays and there are different types of disability among them. Physical disability is common and brings serious inconvenience, both physically and mentally. Those with hand or arm amputation are mostly affected. In order to ameliorate their daily lives, some traditional prosthesis like the manipulator, not only makes up for the lack of appearance, but also helps them do some basic daily actions [1]. However, it cannot be controlled and cannot actlike a real limb. In recent years, the emergence of intelligent prosthetic limb solved this difficulty and makes it possible for disabled people to control it more easily and enables it to act like a real limb. How to make the manipulator work as flexibly as a real hand is now a key point [2]. The motor axons and their branches in the human cerebral cortex generate raw control signals and transmit them to the motor endplate. Electromyography (EMG) signals are formed here and stimulate muscle fibers to produce actions. EMG signals contain a number of information related to actions, and features need be extracted and input into the classifier for action recognition. The manipulators controlled by EMG signals are usually designed according to the fingers and palms of the human hand and have several degrees of freedom (DOFs). The actions of manipulators are based on control commands corresponding to the labels of the classifiers [3,4]. [PERSON] [5] put forward a feature, termed as a magnitude-angle (MA) feature, to indicate the most active muscle groups, while subjects preformed hand motions. Then, the team proposed the concept of classifier feedback to allow patients to learn to generate repeatable sEMG pattern. Especially when a visual trajectory feedback (VTF) training strategy was applied, the averaged hand motion classification accuracy using linear discriminant analysis (LDA) across 10 sessions and 6 subjects was 80.4 \(\pm\) 6.0%. [PERSON] and [PERSON] [6] extracted high-order statistics of the arm's EMG signals and realized the classification of 4 motions with an accuracy of 91%, through a cluster analysis classifier. [PERSON] et al. [7] acquired 8-channel EMG signals through an MYO bracelet, extracted 5 kinds of features, and judged 4 finger motions through a back propagation (BP) neural network, with an accuracy of 90.35%. [PERSON] et al. [8] acquired four-channel sEMG signals on the human forearm, reduced the dimensions, and classified 8 actions through LDA. The accuracy reached 98.12%. [PERSON] et al. [9] acquired four-channel EMG signals, adopted sliding full-cycle filtering, and utilized dynamic fuzzy neural networks to classify actions, obtaining an accuracy of up to 96.7%. [PERSON] et al. [10] extracted the root-mean-square (RMS) and fourth-order autoregressive (AR) model coefficients, and used LDA to classify 6 gestures. The accuracy rates of 4 channels and 8 channels were 90% and 96%, respectively. [PERSON] et al. [11] made a wearable EMG acquisition device with an accuracy of 98.55%, for recognizing 4 motions. [PERSON] [12] presented a novel subclass discriminant analysis (SDA) algorithm and a principal component analysis (PCA) strategy for the prediction of simultaneous wrist rotation and finger gestures. The group demonstrated that a wrist tracking precision (coefficient of determination) of 0.954 \(\pm\) 0.012 and a finger gesture classification accuracy of 96.5 \(\pm\) 1.7% could be simultaneously achieved. [PERSON] [13] proposed a closed loop Functional Electrical Stimulation (FES) system, using sEMG bias feedback from the bilateral arms for enhancing upper limb stroke rehabilitation. The RMS value was taken as a measure of the power of sEMG signals, which is related to constant force and non-fatiguing contraction. [PERSON] [14] proposed a spatial-temporal features-based gesture recognition method (STF-GR) and conducted experiments on 3 datasets.