Design and Analysis of a Smart Rehabilitation Walker With Passive Pelvic Mechanism
Bibliographic record
Abstract
Abstract In response to the ever-increasing demand of community-based rehabilitation, a novel smart rehab walker iReGo is designed to facilitate the lower limb rehabilitation training based on motion intention recognition. The proposed walker provides a number of passive degrees-of-freedom (DoFs) to the pelvis that are used to smooth the hip rotations in such a way that the natural gait is not significantly affected, meanwhile, three actuated DoFs are actively controlled to assist patients with mobility disabilities. The walker first identifies the user’s motion intention from the interaction forces in both left and right sides of the pelvis and then uses the kinematic model to generate appropriate driving velocities to support the body weight and improve mobility. In this paper, workspace, dexterity, and the force field of the walker are analyzed based on the system Jacobian. Simulation and experiments with healthy subjects are carried out to verify the effectiveness and tip-over stability. These results demonstrate that the walker has sufficient workspace for pelvic motions, satisfactory dexterity, and near-linear force feedback within the prescribed workspace, and that the walker is easily controlled to ensure normal gait.
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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 | 0.000 |
| 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) | 0.002 | 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".