Investigating Classification Parameters for Continuous Myoelectrically Controlled Prostheses
Bibliographic record
Abstract
This work is part of the ongoing research on dexterous and natural control of upper extremity prostheses using myoelectric signals (MES). An extensive database of thirty subjects using eight channels of MES from the right arm was developed. Data were collected from each subject in four sessions on separate days, with each session containing six trials. Each trial consisted of seven limb movements (hand open, hand close, wrist flexion, wrist extension, supination, pronation and rest) repeated four times, held for three seconds, in a random sequence. Electrodes were placed on right forearm at muscle sites determined by human physiology of the limb movements and one electrode was placed on the bicep muscle of the right arm. This database will serve as necessary input for the future development and testing of classification algorithms, including evaluating different classification techniques, comparing different feature sets, and investigating intra- and inter-session variabilities. In this paper, the effect of channel placement is investigated. This investigation is performed by comparing classification accuracies using all eight MES channels with various subsets of channels. Results will provide an indication of which muscle sites are important and how many channels are necessary to maintain a high degree of classification accuracy.
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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 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".