Leveraging Generative Adversarial Networks for Synthetic EMG Data Generation: Towards Efficient Device Validation

Electromyography (EMG) sensors are widely used in various applications, including healthcare, human-computer interaction, and rehabilitation. Validating the performance of EMG devices often requires extensive data collection from multiple human subjects, which can be time-consuming and resource-intensive. In this study, we present a novel approach to address this challenge by leveraging Generative Adversarial Networks (GANs) to generate synthetic EMG data that closely resembles real-world signals. By transforming the original EMG data from a human subject into a 2D image format, we trained a GAN model to learn the underlying characteristics of the EMG signal and generate new, similar images. Our results demonstrate that the artificial EMG data generated by the GAN is statistically indistinguishable from the original EMG data, as evidenced by the visual similarity between the generated and real images.