Physiologically Based Controller for Generating Overground Locomotion Using Functional Electrical Stimulation
Bibliographic record
Abstract
The physiological control of stepping is governed both by signals descending from supraspinal systems and by circuitry residing within the lumbosacral spinal cord. The goal of this study was to evaluate the capacity of physiologically based controllers to restore functional overground locomotion after neurological damage, such as spinal cord injury when used in conjunction with functional electrical stimulation. For this purpose we implemented and tested two controllers: 1) an intrinsically timed system that generated a predetermined rhythmic output and 2) a sensory-based system that used feedback signals to make appropriate transitions between the unloaded (flexion) and loaded (extension) phases of the gait cycle. A third controller, a combination of the intrinsically timed and sensory-driven controllers, was implemented and two sessions were conducted to demonstrate the functional advantages of this approach. The controllers were tested in anesthetized cats, implanted with intramuscular electrodes in six major extensor and flexor muscles of the hindlimbs. The cats were partially supported on a sliding trolley that was propelled by the hindlimbs along a 2.5-m instrumented walkway. Ground reaction forces and limb positions were measured by force plates in the walkway and by accelerometers secured to the legs of the cat, respectively. The controllers were used to generate patterns of stimulation that would elicit alternating flexor (swing) and extensor (stance) movements in the hindlimbs. Using either the intrinsically timed or sensory-driven controllers, the cats were able to travel a distance of 2.5 m, taking five to 12 steps. Functional stepping sequences were more easily achieved using the intrinsically timed controller as the result of a lower sensitivity to the selection of initial stimulation parameters. However, unlike the sensory-driven controller, the intrinsically timed controller was unable to adjust to overcome walkway resistance and muscle fatigue. Neither system was consistently able to ensure load-bearing stepping. Therefore we propose the use of a "combined controller" that relies heavily on intrinsic timing but that can be reset based on sensory signals. A combined controller such as this one may provide the best solution for restoring robust overground locomotion after 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 | 0.000 |
| 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) | 0.001 | 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".