Kinematic modeling for the assessment of wheelchair user's stability
Bibliographic record
Abstract
A computer kinematic model was developed to simulate the lateral and transverse stabilities of wheelchair users in order to compare the effect of different backrests. This model is composed of ellipsoids and parallelepipeds representing the main components of the human body, the seating devices and the wheelchair. A fifteen-segment three-dimensional (3-D) model linked by spherical and revolute joints was created using the ADAMS software (Mechanical Dynamics, Inc.). Torsional springs and dampers are used at the joints to represent four sets of articulation stiffness. Seating devices are represented with 45 rectangular surface patches. The interface between human body and seating devices is modeled by contact elements, which included the specification of stiffness, damping, and deformation of cushions and buttocks. Simulations of a user and his wheelchair moving at 1.4 m/s on a tilted pathway were performed. Different indexes [trunk lateral tilt (TLT) and trunk transverse rotation (TTR)] were measured and compared to those of a similar experimental study on four subjects. The effect of joint stiffness was quantified and a sensitivity study showed the importance of the hip, neck, lumbar, and thoracic joint stiffness on model response (between 16% and 68%). Two backrests (standard and highly contoured) were tested with the kinematic model and their stability compared. Overall, the coherence between the simulations and the experiments shows that this approach is appropriate to compare various seating devices (maximal difference of 1.3 degrees between the simulated and experimental curves for the intermediate joint stiffness sets). The smallest rotations of the highly contoured backrest (6.3 degrees versus 8.9 degrees for TLT and 3.9 degrees versus 6.7 degrees for TTR) suggest that the contouring of the mid torso is more efficient than the lower torso to provide stability to the wheelchair user. This model is an adequate tool to test and improve the design of seating aids.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".