Development of a Real-Time Neural Controller using an EMG-Driven Musculoskeletal Model
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".