CEINMS-RT: An Open-Source Framework for the Continuous Neuro-Mechanical Model-Based Control of Wearable Robots
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Bibliographic record
Abstract
Human movement emerges from the interplay between nervous, muscular, and skeletal systems, interacting with the environment. Understanding these neuro-mechanical processes is crucial for developing volitional, neural control of wearable robots aimed at restoring mobility after neuromuscular injury. Movement neuro-mechanics is often studied via computer models of the neuromusculoskeletal system, which use static, dynamic optimization or reinforcement learning to estimate muscle activation patterns and resulting motor function. However, such approaches often fail at capturing the variability in multi-muscle recruitment and force generation across movements, anatomies, and conditions i.e., ageing or injury. Electromyography (EMG)-driven musculoskeletal modeling, or neuro-mechanical modeling, uses measured EMGs and joint angles for simulating musculotendon force and joint moment generation dynamics, with no assumptions on how muscles are neurally recruited. EMG-driven models have enabled task-agnostic, myoelectric, model-based controllers for devices ranging from bionic arms and legs to trunk, arm and leg exoskeletons. However, real-time myoelectric model-based controllers still remain largely proprietary, hindering their widespread use, progress and standardization. Here, we introduce CEINMS-RT, an open-source, EMG-driven modeling framework for real-time, myoelectric model-based control. Because CEINMS-RT computation time is well below the muscle electromechanical delay (< 3.1ms on a Raspberry Pi 2), it can estimate EMG-dependent joint moments in advance, an essential requirement for volitional robotic control, while maintaining accuracy comparable to offline models. This provides an open-source, mechanistic alternative to neural networks that directly map EMGs into joint moment or kinematic profiles, without modeling intermediate neuro-mechanical variables that are critical for understanding movement and human-robot interaction (e.g., musculotendon kinematics and impedance).
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it