Development of a real-time neural controller using an EMG-driven musculoskeletal model

Abstract

Here we present our development of a novel real-time 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 an initial proof of concept, we demonstrated that our system could control a robot actuator across a range of movements, both static and dynamic, and at different operating speeds, achieving high reference tracking performance and state-of-the-art processing times of 2.9 ms, important for real-time embedded computing. This research helps lay the groundwork for next-generation neural-machine interfaces that are fast, accurate, and adaptable to diverse users and control applications.