Kinematics-based prediction of trunk muscle activity in response to multi-directional perturbations during sitting
Bibliographic record
Abstract
Recent work suggests that functional electrical stimulation can be used to enhance dynamic trunk stability following spinal cord injury. In this context, knowledge of the relation between trunk kinematics and muscle activation in non-disabled individuals may assist in developing kinematics-based neuroprostheses. Our objective was therefore to predict the activation profiles of the major trunk muscles from trunk kinematics following multi-directional perturbations during sitting. Trunk motion and electromyograms (EMG) from ten major trunk muscles were acquired in twelve non-disabled, seated individuals who experienced a force of approximately 200 N applied to the trunk in eight horizontal directions. A linear, time-invariant model with feedback gains on angular trunk displacement, velocity, and acceleration was optimized to predict the EMG from trunk kinematics. For each muscle, only the three directions that produced the largest EMG response were considered. Our results indicate that the time course of the processed EMG was similar across muscles and directions and that the model accounted for 68-92% of the EMG variance. A combination of neural and biomechanical mechanisms associated with trunk control can explain the obtained model parameters. Future work will apply the gained insights in the design of movement-controlled neuroprostheses for facilitating trunk stability following spinal cord injury.
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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) | 0.000 | 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) | 0.000 | 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 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".