A myoelectric digital twin for fast and realistic modelling in deep learning

Muscle electrophysiology is a promising tool for human-machine approaches in medicine and beyond clinical applications. The authors propose here a model simulating electric signals produced during human movements and apply this data for training of deep learning algorithms. Muscle electrophysiology has emerged as a powerful tool to drive human machine interfaces, with many new recent applications outside the traditional clinical domains, such as robotics and virtual reality. However, more sophisticated, functional, and robust decoding algorithms are required to meet the fine control requirements of these applications. Deep learning has shown high potential in meeting these demands, but requires a large amount of high-quality annotated data, which is expensive and time-consuming to acquire. Data augmentation using simulations, a strategy applied in other deep learning applications, has never been attempted in electromyography due to the absence of computationally efficient models. We introduce a concept of Myoelectric Digital Twin - highly realistic and fast computational model tailored for the training of deep learning algorithms. It enables simulation of arbitrary large and perfectly annotated datasets of realistic electromyography signals, allowing new approaches to muscular signal decoding, accelerating the development of human-machine interfaces.

Automated summary

Muscle electrophysiology is a promising tool for human-machine interfaces in medicine and beyond clinical domains. In this study, the authors propose a model to simulate electric signals during human movements and use the data to train deep learning algorithms. Muscle electrophysiology has proven to be a powerful tool for driving human-machine interfaces, with applications in robotics and virtual reality. However, more sophisticated decoding algorithms are needed to meet the requirements of fine control in these applications. The concept of Myoelectric Digital Twin is introduced as a highly realistic and fast computational model for training deep learning algorithms, enabling simulation of large and perfectly annotated datasets of realistic electromyography signals.