MMG-based multisensor data fusion for prosthesis control
Bibliographic record
Abstract
Advantages in the functionality and comfort of soft silicon sockets or roll-on sleeves over polyester laminate hard sockets for upper-limb prosthesis have been consistently reported. However, attachment and wire breakage issues prevent the use of electromyography (EMG) sensors with soft sockets in electrically powered prosthesis for below-elbow amputees. Mechanomyography (MMG) is the measurement of the mechanical vibrations elicited by contracting muscles. The use of MMG sensors embedded within the soft silicon socket solves current attachment issues with EMG sensors and facilitates distal recording preventing wire breakage. In order to implement a practical MMG-based detection system of muscle contractions for prosthesis control, three silicon-embedded microphone-accelerometer sensor pairs were used to record MMG signals and movement artifact around the distal end of the residual limb of a below-elbow amputee. A multisensor data fusion strategy for the generation of binary control signals based on the root-mean-square (RMS) values of the segmented signals acquired with each transducer was trained and used as a detector. A ninety five (95%) and eighty six percent (86%) accuracy were achieved in the detection of contraction signals from the wrist extensors and flexors respectively. The error in the discrimination of movement artifact was thirteen percent (13%).
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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 | 0.002 |
| 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 | 0.001 |
| 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".