Restoring limb motor function in individuals with spinal cord injury (SCI), stroke, or amputation remains a critical challenge, one which affects millions worldwide. Recent studies show through surface electromyography (EMG) that spared motor neurons can still be voluntarily controlled, even without visible limb movement . These signals can be decoded and used for motor intent estimation; however, current wearable solutions lack the necessary hardware and software for intuitive interfacing of the spared degrees of freedom after neural injuries. To address these limitations, we developed a wireless, high-density EMG bracelet, coupled with a novel software framework, MyoGestic. Our system allows rapid and tailored adaptability of machine learning models to the needs of the users, facilitating real-time decoding of multiple spared distinctive degrees of freedom. In our study, we successfully decoded the motor intent from two participants with SCI, two with spinal stroke , and three amputees in real-time, achieving several controllable degrees of freedom within minutes after wearing the EMG bracelet. We provide a proof-of-concept that these decoded signals can be used to control a digitally rendered hand, a wearable orthosis, a prosthesis, or a 2D cursor. Our framework promotes a participant-centered approach, allowing immediate feedback integration, thus enhancing the iterative development of myocontrol algorithms. The proposed open-source software framework, MyoGestic, allows researchers and patients to focus on the augmentation and training of the spared degrees of freedom after neural lesions, thus potentially bridging the gap between research and clinical application and advancing the development of intuitive EMG interfaces for diverse neural lesions.

翻译：恢复脊髓损伤、中风或截肢患者的肢体运动功能仍是一项关键挑战，影响着全球数百万人。近期研究表明，即使在没有可见肢体运动的情况下，通过表面肌电图仍可检测到保留的运动神经元能够被自主控制。这些信号可被解码并用于运动意图估计；然而，当前的可穿戴解决方案缺乏必要的硬件和软件，难以在神经损伤后对保留的自由度进行直观接口交互。为突破这些限制，我们开发了一种无线高密度肌电手环，并结合新型软件框架MyoGestic。我们的系统支持机器学习模型根据用户需求进行快速定制化适配，实现对多个保留特征自由度的实时解码。在本研究中，我们成功实时解码了两名脊髓损伤患者、两名脊髓中风患者及三名截肢者的运动意图，受试者在佩戴肌电手环后数分钟内即可实现多个可控自由度。我们通过概念验证表明，这些解码信号可用于控制数字渲染手部模型、可穿戴矫形器、假肢或二维光标。本框架采用以参与者为中心的设计理念，支持即时反馈整合，从而促进肌电控制算法的迭代优化。所提出的开源软件框架MyoGestic使研究者和患者能够专注于神经损伤后保留自由度的增强与训练，有望弥合科研与临床应用之间的鸿沟，推动针对不同神经损伤的直观肌电接口技术发展。