sEMG Signal Generation for Data Augmentation Using Time Series Transformer Based Conditional GAN

Medical data like physiological signals or others, are usually hard to collect, label and share. This is a huge problem because the unavailability of medical datasets will limit the development of machine learning models that can be of big benefit in the medical field. Electromyography (EMG) signals are a type of physiological signals that when available, can be used to train predictive models for motion recognition or muscle assessment. Collecting the EMG data can be hard due to the rarity of some diseases or the measuring being unachievable because of the condition of the patient (e.g. not being able to walk or properly contract their muscle). And finally, sharing these datasets is very limited due to privacy concerns where the identity of the source can be leaked which is a crucial problem. In this preliminary study, our aim is to present a solution to provide sEMG datasets that are big enough in size to train machine learning models. We explore a transformer based GAN model to create synthetic sEMG signals that can replace the real data and try to solve all the problems discussed above. In the first part of this work, we will discuss the generation process, and in the second part, the evaluation of the created ‘fake’ signals.