# Surface Electromyography-Based Action Recognition and Manipulator Control
## 1 Introduction
Experimental evaluations with within-subject tests showed that STF-GR exceeded the performance of other state-of-the-art methods, including deep learning algorithms that were not focused on spatial-temporal features and traditional machine learning algorithms that used handcrafted features. [PERSON] [15] built a new motion recognition model based on deep learning to improve the accuracy of motion recognition of sEMG signals. The model used an architecture that included 6 convolutional layers and 6 pooling layers. The results showed that an accuracy of about 90% could be achieved when using sEMG signal data from every single subject or all subjects. [PERSON] [16] analyzed HD-sEMG signals recorded from 32 channels during Sit to Stand (STS) test for the functional assessment of body muscles. Five primary characteristic features, iEMG, ARV, RMS, Skewness, and Kurtosis, were employed for the study. A channel clustering approach was proposed based on the parameters using Non Negative Matrix Factorization (NMF). The NMF-based clustering of the HD-sEMG channels seemed to be sensitive toward modifications of the muscle activation strategy with ageing during the STS test. [PERSON] [17] designed a multi-channel EMG module and developed an algorithm for classifying different types of hand postures, using the artificial neural network (ANN). The classification accuracy was evaluated for 10 normal volunteers. The results revealed an optimized accuracy of 97.49 \(\pm\) 3.87%, after using a high independence feature group (HIFG). [PERSON] [18] presented an HRI system based on 8 fully differential EMG sensors connected to a wearable sensor node for acquisition and processing. The results of the test performed on 4 able-bodied subjects showed success rates greater than 90%, in grasping objects that required different hand shapes and impedance regulations for the task completion. In addition to the exact feature and classifier, the analysis of different forms of data also determined the speed of signal processing. The transient and steady-state data of raw signals could both be detected in pattern recognition. Usually, the whole EMG signals are collected and the transient data inside are detected for feature extraction and classification. However, this brings obvious disadvantages as all data are needed to be processed in each classification, resulting in a slow speed. The severe delay does not meet the expectation for real-time control. Hence, dividing the whole EMG signals into windows is presented [19]. The action classification is performed by judging the steady-state data in each window rather than processing the whole data. Action recognition is achieved with only a small length of data and the response time experiences a dramatical reduction, meanwhile. [PERSON] et al. [20] processed transient and steady-state sEMG data and analyzed the classification effect, respectively. Subsequently, the team concluded that compared to transient data, steady-state data could lead to a higher accuracy of action classification and less delay in real-time control. [PERSON] et al. [21] applied steady-state data in sliding windows to classify actions and also obtained high classification accuracy, thus, demonstrating the effectiveness of sliding window analysis. Therefore, analyzing the steady-state data of small windows in EMG signals provides a possibility that the biosignals and manipulator could be well combined for free-style control in real time. Although some research significantly increased the accuracy of offline action classification, there are still few studies that could achieve a high accuracy in real-time classification and control. Additionally, reducing the delay as much as possible also contributes to an increase in the synchronization of control. To address the problem, we present a multifunctional sEMG detection and action recognition system that integrates signal acquisition, signal processing, and real-time control.