Finite state controller for functional electrical stimulation: software implementation
Bibliographic record
Abstract
Finite state machine provides a flexible framework to design controllers for Functional Electrical Stimulation (FES). The finite state machine controller allows the implementation of different control strategies under different states. This paper describes the software implementation of finite state machine controller for a portable FES stimulator based on 68332 microcontroller. The states, actions and state transition rules are defined in a text based controller definition file which is edited by users and downloaded to the portable FES stimulator from PC via serial RS232 link. The definition file also defines the stimulation output channels, sensor input channels, and constants which are used in actions and rules. Several commonly used stimulation actions such as pulsewidth/frequency change, pulsewidth ramp are pre-defined. More complex controllers like fuzzy logic controller or PID controller can be incorporated into the finite state controller using customized C controller function. This finite state FES controller is easy to use for ordinary users without computer programming knowledge, yet flexible enough to incorporate complex control functions for expert users who can write the customized C controller function.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.029 | 0.006 |
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".