Human and Passive Lower-Limb Exoskeleton Interaction Analysis: Computational Study with Dynamics Simulation using Nonlinear Model Predictive Control
Bibliographic record
Abstract
Forward dynamics simulations have the advantage of assessing performance of novel exoskeleton designs at a low cost. For developing a new passive lower-limb exoskeleton, the simulation needs to represent the sitting posture in which the wearer performs working tasks while maintaining balance with the whole body. The present study constructed a forward dynamics simulation for analyzing and developing a new passive lower-limb exoskeleton; the validity of the simulation was investigated using experimental data. The present method computes the interactions between the exoskeleton and wearer, such as reaction forces, physical posture, and physical load, based on the forward dynamics simulation driven by nonlinear model predictive control (NMPC). The NMPC cost function consisted of the physical load and the fitness of working task with constraints to evaluate balance. As a result, the present simulation represented the characteristic posture when sitting on the exoskeleton in which the wearer performs the working task while maintaining balance with the whole body. However, the simulation computed an upright posture of the lumbar joint that differed from the experimental results and needs to be improved. In future work, the simulation will be modified for representing the valid physical posture when wearing the exoskeleton, such as simulating the physical motion of the same working task as in the experiment, and modeling the interaction between the human, exoskeleton, and ground.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".