# LSTM Recurrent Neural Network for Hand Gesture Recognition Using EMG Signals
###### Abstract
The main data that it needs are images, either if it is a sequence of them (Dynamic movement classification) or just one of them (Static movement classification) [15], but there is an even lower level approach to this particular data set: Images are large matrices composed of RGB (Red, Green, Blue) pixel data. Then these pixels are the basic structural and functional units of an image. That is why some studies dig even deeper in to this kind of data and add depth to the image composition. Such grids are formed with RGB-D pixels [16,17]. This can be accomplished by using commercially available cameras such as the ones that can be found in a Kinect [18] or in the commercially available leap motion controller [19]. The next step in this kind of classification is to process those large grids of pixels and to obtain a good fitting model, capable of sorting different types of gestures. Some of those are based on Convolutional Neural Networks (CNN) [16,20], K-Nearest Neighbor (KNN) and Bayesian Neural Network [21]. In certain cases the model descriptors are changed in order to lower the computational demands [22,23] for real-time image processing. The second development focus is the use of Electromyography (EMG) for hand gesture recognition. It is important to mention that the EMG shows the electrical muscular activity recorded by positioning electrodes on top of the muscular group of interest. In order to obtain valuable information for the characterization of the hand gesture, filtering, processing and clustering methods of EMG signal have emerged as highly relevant techniques [24]. Another batch of papers shows a lot of methods and variants, used in the literature, to increase the rate of accuracy of the process mentioned above, starting by using a large amount of electrodes, followed by the use of huge time resolution ADCs (Analog to digital converters), however the real variety in literature comes when it is time to choose the descriptors [24,25,26] and what algorithm to use for sorting gestures, from recurrent neural networks (RNN) [27,28], to Hidden Markov Models [29], all the way to gesture recognition based on Motor Unit Spike Trains (MUST) [30], nearly every algorithm has been used. Nevertheless deep learning models have been the most accurate and reliable, RNN have shown good results along with Long-short Term Memory (LSTM) layer in them [17,27,28]. The third development trend, which is starting to take off, is force myography (FMG). It is basically an alternative for EMG sensing, and it is based on the volumetric changes of muscle fibers while they contract or distract [31]. Some researchers claim that it is even better than EMG because it only needs force or pressure sensors attached to an armband, and the requirements for such signals processing do not demand as many filters or amplifiers as EMG does [29]. Here, the simple nature of both signals is different and that is why the approaches are distinct. Whilst the EMG is an electrical signal caused by small impulses created in the motor neurons when contracting the muscles [24], FMG is a volume change that can be sensed by using special transducers in order to know its magnitude. If a big picture of the process of this kind of hand gesture recognition is taken, much would be similar to the EMG signal acquiring and clustering process. Initially the signal is acquired by using sensors suitable for this approach. It is important to take into consideration the force or pressure range that the transducer is able to sense [32]; then these data are processed and lastly the gestures are classified using a various palette of algorithms such as Linear Discriminant Analysis (LDA) [5; 27] and Recurrent Neural Networks[17].