Development of a Real-Time Neural Controller Using an Emgdriven Musculoskeletal Model.
Summary
This study introduces a new real-time neural controller using EMG signals and a musculoskeletal model for robot control. It achieves fast, accurate, and robust motion control for diverse applications.
Area of Science:
- Biomedical Engineering
- Robotics
- Neuroscience
Background:
- Electromyography (EMG) control systems face challenges in accuracy, latency, and robustness.
- Existing systems struggle with both isometric and non-isometric muscle contractions.
- Human motor control offers insights for adaptive and smooth robotic interactions.
Purpose of the Study:
- To develop a novel real-time neural controller for volitional robot and computer control.
- To improve EMG control by addressing accuracy, latency, and robustness.
- To enable seamless motion control during various muscle contraction types.
Main Methods:
- Developed an EMG-driven musculoskeletal model for neural command translation.
- Combined EMG signal processing, neural activation dynamics, and Hill-type muscle modeling.
- Integrated muscle activation dynamics with impedance control for adaptive interactions.
Main Results:
- Achieved high reference tracking performance in controlling a robotic actuator for lower-limb movements.
- Demonstrated state-of-the-art processing time of 2.9 ms for real-time embedded computing.
- Showcased robust control during static and dynamic movements at varying speeds.
Conclusions:
- The novel controller offers fast, accurate, and adaptable performance for EMG-based human-machine interfaces.
- This approach enhances robustness against electrode variability and signal noise.
- Lays the groundwork for next-generation neural-machine interfaces in diverse applications.
