# LSTM Recurrent Neural Network for Hand Gesture Recognition Using EMG Signals
## 4 Conclusions and Future Work  
The proposed LSTM-RNN model for gesture classification proved to be a suitable approach for gesture recognition using EMG signals. The LSTM-RNN model was able to obtain high accuracy during training and validation phase; indicating that the model was able to learn the patterns in the EMG signals and generalize on new data. Furthermore, it is important to highlight that the model is small enough to be embedded in a micro-controller, which is fundamental to implement it for a commercial or industrial application. The proposed LSTM-RNN model was able to perform at a similar level of accuracy than other state-of-the-art RNN models while reducing the model complexity and the number of EMG channels, increasing its potential to be embedded and its commercial scalability. With this in mind, the proposed model shows promising results for its industrial or commercial application, even though further improvements must be made to achieve this development state, particularly in the EMG acquisition system. This research allowed to go beyond static data classification, giving valuable insights regarding the challenges of real time classification. It was found that the reduced performance during real-time testing was due to the high sensitivity of the model to small changes in the EMG armband position and noise present in the signal when acquiring the EMG. However, although the model accuracy varies with different postures of the acquisition system, it is still able to generalize to new data. With this in mind, having a better EMG acquisition device and a better acquisition \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Work** & **Channels** & **Classifier** & **Test Accuracy** \\ \hline Samadani [36] & 12 & Stacked LSTM & 89.50\% \\ \hline [PERSON] et al. [35] & 8 & BILSTM-AT & \(91.40\pm 3.00\%\) \\ \hline [PERSON] et al. [35] & 8 & Stacked GRU & 89.60\% \\ \hline [PERSON] et al. [39] & 8 & Stacked LSTM & 97.10\% \\ \hline [PERSON] et al. [41] & 16 & LSTM & 78.13\% \\ \hline [PERSON] et al. [42] & 12 & LSTM-Dense & 75.50\% \\ \hline [PERSON] et al. [37] & 10 & CNN-RNN & 87.00\% \\ \hline
**Ours** & **4** & **LSTM-Dense** & \(\mathbf{87.29\pm 6.94\%}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of the proposed LSTM-RNN model with state-of-the-art RNN models found in literature. process, where noise and variations in position across different sessions are minimized, will probably improve the model performance considerably, without the need of changing the model. In general, the proposed approach using deep neural models its effective for finding patterns in stochastic signals like EMG signals, making deep learning models a great tool for gesture recognition tasks based on this type of signals. It is important to note that in order to build an effective and robust gesture recognition system, not only is important to build and optimize a good model, it is also fundamental to acquire quality data and perform a good pre-processing of it. It is only when an optimal point between data and model is found that the overall system can find the best performance. In future work the EMG acquisition armband will be upgraded with the objective of acquiring more data and improving its quality; new pre-processing and feature extraction techniques will be tried, aiming to reduce the system sensitivity to noise in the EMG signal and variations in the acquisition devise; moreover, the gesture recognition model will be optimized with the new data and it will be embedded in a microcontroller in order to control an active hand prosthesis using different hand gestures. The authors' contributions to the achievement of the research are as follows: Model design, conceptualisation, computational modelling and Analysis of Results, A.T.-O., J.J.-T.; Analysis of Results, processing and review, J.C.T., A.P., A.L.-G.; Analysis of Results and review R.A.C. All authors have read and agreed to the published version of the manuscript.