Adaptive logic networks in rehabilitation of persons with incomplete spinal cord injury
Bibliographic record
Abstract
Persons with incomplete spinal cord injury are generally at least partially paralyzed and are often unable to walk. Manually-controlled electrical stimulation has been used to act upon nerves or muscles to cause leg movement so such persons can achieve functional walking. They use crutches or a mobile walker for support, and initiate each stimulus by pressing a button. Artificial intelligence and machine learning techniques are now making it possible to automate the process of stimulus-initiation. Supervised training of an automatic system can be based on samples of correct stimulation given by the patient or by a therapist, accompanied by data from sensors indicating the state of the person`s body and its relation to the ground during walking. A major issue is generalization, i.e. whether the result of training can be used for control at a later time or in somewhat different circumstances. As the possibilities grow for increasing the number and variety of sensors on a patient, and for easily implanting more numerous stimulation channels, the need is increasing for powerful learning systems which can automatically develop effective and safe control algorithms. This paper explains the foundations of adaptive logic networks, and illustrates how they have been used to develop an experimental walking prosthesis used in a laboratory setting. Successful generalization has been observed using parameters from training which took place minutes to days earlier.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".