| Συγγραφείς: |
Chen C; State Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, P. R. China.; Shanghai Key Laboratory of Intelligent Robotics, Shanghai Jiao Tong University, Shanghai, P. R. China., Guo R; State Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, P. R. China., Li D; State Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, P. R. China., Shi S; State Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, P. R. China., Guo W; Shanghai Key Laboratory of Intelligent Robotics, Shanghai Jiao Tong University, Shanghai, P. R. China.; Meta Robotics Institute, Shanghai Jiao Tong University, Shanghai, P. R. China., Meng J; State Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, P. R. China.; Shanghai Key Laboratory of Intelligent Robotics, Shanghai Jiao Tong University, Shanghai, P. R. China., Gu G; State Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, P. R. China.; Shanghai Key Laboratory of Intelligent Robotics, Shanghai Jiao Tong University, Shanghai, P. R. China., Zhu X; State Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, P. R. China.; Shanghai Key Laboratory of Intelligent Robotics, Shanghai Jiao Tong University, Shanghai, P. R. China.; Meta Robotics Institute, Shanghai Jiao Tong University, Shanghai, P. R. China. |
| Περίληψη: |
Accurate decoding of movement intent from muscle signals is essential for dexterous prosthetic control. While motoneuron discharge decomposition provides a promising approach, most studies are confined to proof-of-concept demonstrations due to the lack of robust, dexterous control strategies, and the complexity of systems involved. Here, we present a motoneuron discharge-driven interface that integrates wireless recording of high-density surface electromyography, real-time motoneuron spike train decomposition, continuous multi-degree-of-freedom (DoF) motion decoding in a prosthetic system, enabling simultaneous and proportional myoelectric control in real-world settings. We validated this system with six trans-radial amputees across a series of functional multi-DoF tasks. The proposed interface achieved accurate and robust control of three-DoF wrist and hand movements, outperforming conventional myoelectric methods in task efficiency. Furthermore, the interface requires only single-DoF calibration data, minimizing user training burden. This study represents the practical demonstration of motoneuron-driven interfacing in end-user applications, highlighting its translational potential for clinical adoption. |