Maximizing the Information Transfer Rate of a Myoelectric Classification System for Individuals With Spinal Cord Injury
Bibliographic record
Abstract
Spinal cord injury (SCI) has a devastating impact on the lives of both those with the injury and those surrounding them. Myoelectric control systems have the potential to help control complex assistive technologies and enhance independence for the users, but do not take into consideration the diversity of neurological impairment observed following SCI. The objectives of this study were: 1) demonstrate that optimizing the design parameters of a myoelectric control system (number of gestures, gesture rate) for each participant can increase the information transfer rate (ITR), and 2) characterize the relationship between the optimal design parameters and the pattern of impairment. The ITR was used to capture trade-offs between increasing the number of possible gestures and gesture rate with the resulting drops in classification accuracy. Ten uninjured and ten participants with SCI were recruited. Using an 8-channel myoelectric control system, a series of trials were performed where the ITR was determined for different combinations of gesture numbers and gesture rates. A significant improvement in ITR was observed after optimization to the individual's level of injury in the SCI group ( $25.8\pm 10.9$ to $31.8\pm 8.0$ bits/min, p =0.002). Significant correlations were observed between the optimal gesture number and multiple metrics of impairment. These results demonstrate that the ITR of a myoelectric classification system can be increased by taking into account the presence of neurological impairment after SCI.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".