Cascaded Adaptation Framework for Fast Calibration of Myoelectric Control
Bibliographic record
Abstract
In spite of several decades of intensive research and development, the existing algorithms of myoelectric pattern recognition (MPR) are yet to make significant clinical and commercial impact. This study focuses on the one of the limiting factors of current algorithms: degradation of algorithm performance due to the inherent non-stationarity in electromyography (EMG) signals and the consequent need for frequent re-training and re-calibration. In order to reduce the re-calibration time required for donning/doffing between sessions and avoid to need to re-calibrate within a given session while donning, we propose a cascaded adaptation (CA) framework based on linear discriminant analysis (LDA), which automatically incorporates models from previous sessions in the model calibration for the current session. The framework also updates the model parameters according to new data samples and the corresponding recognized labels. Both off-line analysis (with data from eight intact-limbed subjects and three trans-radial amputees) and online testing with 9 intact-limbed subjects were conducted to evaluate the proposed method. Results show that the LDA embedded with CA (LDA-CA) is able to classify 11 types of motion with a small training data set, beginning from the second session of the experiment. The proposed LDA-CA obtains better performance as compared with three other methods-baseline LDA (LDA-BL), LDA with self-enhancing (LDA-SE), and LDA with domain adaptation (LDA-DA). The online test demonstrates that LDA-CA requiring an initial 1 min training session can be reliably used for 8 h without re-training. The proposed myoelectric control framework with low calibration burden has the potential to move the MPR based prostheses from academic research to clinical application.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".