Genetic Algorithms Based Approach for Designing Spring Brake Orthosis – Part Ii: Control of FES Induced Movement
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
Spring brake orthotic swing phase for paraplegic gait is initiated through releasing the brake on the knee mounted with a torsion spring. The stored potential energy in the spring, gained from the previous swing phase, is solely responsible for swing phase knee flexion. Hence the later part of the SBO operation, functional electrical stimulation (FES) assisted extension movement of the knee has to serve an additional purpose of restoring the spring potential energy on the fly. While control of FES induced movement as such is often a challenging task, a torsion spring, being antagonistically paired up with the muscle actuator, as in spring brake orthosis (SBO), only adds to the challenge. Two new schemes are proposed for the control of FES induced knee extension movement in SBO assisted swing phase. Even though the control schemes are closed-loop in nature, special attention is paid to accommodate the natural dynamics of the mechanical combination being controlled (the leg segment) as a major role playing feature. The schemes are thus found to be immune from some drawbacks associated with both closed-loop tracking as well as open-loop control of FES induced movement. A leg model including the FES knee joint model of the knee extensor muscle vasti along with the passive properties is used in the simulation. The optimized parameters for the SBO spring are obtained from the earlier part of this work. Genetic algorithm (GA) and multi-objective GA (MOGA) are used to optimize the parameters associated with the control schemes with minimum fatigue as one of the control objectives. The control schemes are evaluated in terms of three criteria based on their ability to cope with muscle fatigue.
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 | 0.000 | 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) | 0.000 | 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