Lately, there has been a lot of focus on using physiological data for human–machine interfaces, especially in creating prosthetic devices like robotic hands. However, when it comes to medical applications, this technology hasn’t caught up with the progress seen in 2D medical imaging. In light of the recent advancements in deep learning (DL) and computer vision, we’ve developed a capsule network for EMG signals, a new method for recognizing hand gestures using Capsule Networks (CapsNet). Our study aims to understand how CapsNet work within the network and assess their effectiveness with different input features. We’ve compared their performance to other machine learning techniques, using the NinaPro DB5 dataset, which contains signals from ten people performing various hand movements. The results of our experiments clearly demonstrate the power of CapsNet, outperforming traditional methods like Random Forest, Support Vector Machine, Fully Connected Network, and Convolutional Neural Network (CNN), achieving an impressive accuracy rate of 93.74% compared to the latest model which gave an accuracy of 90.1% on the same dataset.