A nonlinear modelling approach to quantify sitting control in individuals with sensorimotor impairments
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Few biomechanical models of sitting stability have been proposed over the last decades and most of them control the trunk position through a lumbar torque. Unfortunately, this type of model is not valid for individuals living with a complete thoracic spinal cord injury (SCI) who generally experience paralysis of their abdominal and lower back muscles. Instead, individuals with SCI often engage their upper limbs as a compensatory strategy to control their sitting position. A new nonlinear biomechanical model is introduced to take into consideration the influence of the upper limbs for sitting control study of people living with SCI. The inherent nonlinearity of the model is taken into account via the Takagi–Sugeno (T‐S) framework. To estimate the internal controlling torques without measurements, an unknown input observer (UIO) is created. Its convergence is expressed by linear matrix inequalities (LMI), which are solved by convex optimization techniques. Numerical simulations with perturbations are used to assess the adequacy of the methodology and preliminary experimental data of one person living with SCI performing a sitting stabilization exercise is used to estimate internal torques of the upper limbs. The main contribution of this work is to provide a way to estimate human joint torques without invasive measurements; the results highlight the validity of both goals of this article, the nonlinear biomechanical modelling and the UIO methodology.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it