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 processes is crucial for developing wearable robotic technologies to restore movement following neuro-muscular injuries. Movement neuro-mechanics is often studied via computer models of the composite neuromusculoskeletal system, which use static, dynamic optimization or reinforcement learning to estimate muscle activation and resulting mechanical forces from kinematic and kinetic data. However, such approaches often fail at capturing the variability in multi-muscle neural recruitment and force generation across movements, anatomies, and conditions (i.e., ageing or injury). Electromyography (EMG)-driven musculoskeletal modeling uses measured EMGs and joint angles for simulating muscle-tendon level mechanics with no assumptions on how muscles are recruited by the central nervous system. EMG-driven models enabled task-agnostic, myoelectric model-based controllers for bionic limbs and exoskeletons. However, real-time neuromechanical models still remain largely proprietary, hindering their widespread use, progress and standardization. Here, we introduce CEINMS-RT, a freely available, open-source, neuromechanical modeling framework for the real-time myoelectric model-based control of wearable robots, including exoskeletons, exosuits, haptic devices, and bionic limbs. CEINMS-RT explicitly models person-specific movement neuro-mechanics and estimates EMG-dependent variables including muscle activation, muscletendon force, and resulting joint dynamics. This represents an open-source alternative to end-to-end neural regressors, which do not estimate intermediate biomechanicasl variables that would be critical for roboust wearable robot control (e.g., joint stiffness, damping or underlying muscle-tendon impedance). Here, we introduce the CEINMS-RT framework and provide application results in the context of wearable robotic control.
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 | 0.003 |
| 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 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.019 | 0.006 |
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".