Neurodec, Sophia Antipolis, France.
Department of Bioengineering, Imperial College London, London, UK.
Nat Commun. 2023 Mar 23;14(1):1600. doi: 10.1038/s41467-023-37238-w.
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.
肌肉电生理学已经成为驱动人机接口的强大工具，除了传统的临床领域，如机器人技术和虚拟现实，还有许多新的近期应用。然而，需要更复杂、更功能和更强大的解码算法来满足这些应用的精细控制要求。深度学习在满足这些需求方面显示出了很高的潜力，但需要大量高质量的注释数据，这在获取方面既昂贵又耗时。数据增强使用模拟，这是在其他深度学习应用中应用的策略，但由于缺乏计算效率高的模型，在肌电图中从未尝试过。我们引入了肌电数字孪生的概念——一种专门为深度学习算法训练而定制的高度逼真和快速计算模型。它能够模拟任意大的、完美注释的真实肌电图信号数据集，允许对肌肉信号解码进行新的方法，加速人机接口的开发。
