CEINMS-RT: An Open-Source Framework for the Continuous Neuro-Mechanical Model-Based Control of Wearable Robots
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).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.023 | 0.007 |
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".