Development of a Real-Time Neural Controller Using an Emgdriven Musculoskeletal Model

Here we present the development of a novel realtime neural controller based on an EMG-driven musculoskeletal model, designed for volitional control of robots and computers. Our controller uniquely enables motion control during both isometric and non-isometric muscle contractions. We address several key challenges in EMG control system design, including accuracy, latency, and robustness. Our approach combines EMG signal processing, neural activation dynamics, and Hill-type muscle modeling to translate neural commands into muscle forces, which can enhance robustness against electrode variability and signal noise. Additionally, we integrate muscle activation dynamics with impedance control, inspired by the human motor control system, for smooth and adaptive interactions. As a proof of concept, we demonstrate that our system can control a robotic actuator across a range of lower-limb movements, both static and dynamic, and at different operating speeds, achieving high reference tracking performance and state-of-the-art processing time of 2.9 ms, important for real-time embedded computing. This research helps lay the groundwork for next-generation neu-ral-machine interfaces that are fast, accurate, and adaptable to diverse users and control applications.