Comparative Study of LSTM, GRU, and BRNN Performance: large-Scale Data Analytics (EMG Signal Classification)

Abstract

     Electromyography (EMG) signals play a pivotal role in biomedical applications, such as prosthetic control and human-computer interaction, where advanced classification methods for accurate muscle activity translation are essential. This study evaluates the performance of three neural network architectures: Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Bidirectional Recurrent Neural Network (BRNN) for EMG signal classification. The EMG signals were preprocessed using Digital Wavelet Transform (DWT) with Daubechies 2 wavelet to extract time-frequency features. Experiments were conducted on a large-scale training dataset comprising 672 subject recordings across six hand gestures, enabling a robust, data-driven comparison. The best classification accuracy of LSTM, GRU, and Bidirectional RNN was achieved, corresponding to cD7, with values of 93.04±1.52, 92.72±1.26, and 91.59±0.97, respectively. However, these models exhibited varying degrees of sensitivity to additive noise, particularly at deeper DWT levels. The findings highlight the trade-offs between accuracy and noise tolerance, providing insights for optimizing EMG-based gesture recognition systems in real-world applications. The final analysis confirms that LSTM outperforms the other models for real-time EMG classification, while GRU and BRNN offer a favourable balance between accuracy and computational efficiency. Looking ahead, the effective handling of large-scale, high-dimensional EMG data yields significant improvements in performance, particularly for prosthetic and real-time control applications.