Muscle synergy of biceps brachii and online classification of upper limb posture
Bibliographic record
Abstract
For someone who has suffered from a partial arm amputation, muscular surface electromyographic (EMG) signals are usually used to control a myoelectric prosthesis. To learn how to satisfactorily control the prosthesis, software programs can be very useful. We present here a program, of which 5 EMG signals collected across the biceps brachii are decoded to produce signals that either make a simulator replicate the arm posture or control the position of a small humanoid manipulator. In the program, following a phase where muscle synergies are extracted from a training trial, the learned features are then used to classify the following arm postures taken by the subject. The mean classification performance for 56 different two-class paired arm postures is 94.9% for 2 normal subjects. Following further testing with normal and amputee subjects, the system could eventually be used in rehabilitation centers where upper limb amputees want to use a myoelectric prosthesis.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".