# LSTM Recurrent Neural Network for Hand Gesture Recognition Using EMG Signals
###### Abstract  
Currently, research on gesture recognition systems has been on the rise due to the capabilities these systems provide to the field of human-machine interaction, however, gesture recognition in prosthesis and orthesis has been carried out through the use of an extensive amount of channels and electrodes to acquire the EMG (Electromyography) signals, increasing the cost and complexity of these systems. The scientific literature shows different approaches related to gesture recognition based on the analysis of EMG signals using deep learning models, highlighting the recurrent neural networks with deep learning structures. This paper presents the implementation of a Recurrent Neural Network (RNN) model using Long-short Term Memory (LSTM) units and dense layers to develop a gesture classifier for hand prosthesis control, aiming to decrease the number of EMG channels and the overall model complexity, in order to increase its scalability for embedded systems. The proposed model requires the use of only four EMG channels to recognize five hand gestures, greatly reducing the number of electrodes compared to other approaches found in the literature. The proposed model was trained using a dataset for each gesture EMG signals, which were recorded for 20 s using a custom EMG armband. The model reached an accuracy of to 99% for the training and validation stages, and an accuracy of 87 \(\pm\) 7% during real-time testing. The results obtained by the proposed model establish a general methodology for the reduction of complexity in the recognition of gestures intended for human.machine interaction for different computational devices. g 2022 202  
1. The first challenge involves hand gesture recognition in real-time [6]. Most studies regarding this topic focus primarily in finding novel algorithm-based methods in order to achieve high performance rates in gesture recognition. Nevertheless these scores are obtained in non-real-time tests where factors such as computational efficiency, window length and system overall performance are not taken in to account [7,8]. Furthermore, this kind of hand gesture recognition methods may be suitable for non-mobile applications such as sign language recognition [9], but not for active prosthesis which are mobile and independent systems. 2. The second challenge is to find such a good performance classifier that it can be used in different types of amputees and still has a good accuracy rate in hand gesture recognition [10], moreover, the challenge is to find an appropriate classifier that can deliver good recognition rates in almost every amputee morphology, while keeping the prediction delay low and the overall system computational needs moderate. 3. The third challenge is to narrow the gap between academic and industrial achievements. Both of them, at this point, are heading in totally different ways. While an academic display can be set in a known environment, the industrial one needs to be as robust as possible in order to increase the overall performance of the system when is used by a real world user [11]. Taking these issues, in the scientific literature three well-defined development trends can be seen. The first development trend focuses on the use of images to cluster hand gestures, highlighting the cameras as a common sensor that can be found in a great number of electronic devices nowadays [12], allowing to identify hand gestures both in static and dynamic environments, as well as in real time [13]. The main source of information in this kind of classification is images, which is why large data sets of pictures are taken, most of them containing multiple hands from different subjects, waving different signs and gestures, regardless of whether the model sorts static or dynamic movements [14].