Gait event and user intention detection for FES-control: selecting sensors
Bibliographic record
Abstract
The authors follow a design method for event detectors using the ID3 rule induction algorithm. Rule induction was chosen mainly for two reasons: it ranks the relative importance of sensor signal attribute in detecting an event and, secondly, the reasoning of the algorithm may be understood by humans since the rules are organized in the familiar form of decision tree consisting of IF(...) THEN(...) ELSE(...) statements. This method allows the control system designer the freedom to position a set of available sensors in unobtrusive locations, such as braces, walking aids or the waistband, and operate them in less demanding environments. Furthermore, the method does not require a high level of intuition as to the contribution that each sensor makes to the detection of an event. Indeed, it has been shown that human experts perform poorly relative to the algorithm in ranking the importance of the sensors (C.A. Kirkwood, and B.J. Andrews, Proc. 11th IEEE EMBS Conf., Seattle, USA, p. 1020-1, 1989). Here, the authors describe a procedure in which a reliable event detector/predictor can be developed with a minimum of sensors. It will mimic a paraplegic's skill in using hand switches to control a simple FES walking system, i.e. it will signal the users implicit intention. This example of skill cloning follows that previously described (Kirkwood and Andrews, 1989).
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".