Context-Informed Incremental Learning Improves Throughput and Reduces Drift in Regression-Based Myoelectric Control
Bibliographic record
Abstract
Despite decades of research, commercially available powered myoelectric prostheses continue to use sequential, classification-based control. While regression-based approaches can improve the dexterity offered through simultaneous, independent, and proportional control, current training protocols lack consistency across studies and fail to capture realistic user behaviours, resulting in robustness issues. To address these challenges, this work employs context-informed incremental learning (CIIL) in an unconstrained, velocity-based environment for regression-based myoelectric control. Two new adaptive models, one inspired by previous works (O-CIIL) and one modified to factor in user compliance and behaviours (T-CIIL), were compared with two models trained using traditional screen-guided training. Sixteen participants completed an online Fitts' Law target acquisition task. Both adaptive approaches significantly outperformed ( ${p}\lt {0}.{05}$ ) the non-adaptive models across a variety of metrics. Additionally, T-CIIL outperformed O-CIIL in alleviating drift and action interference, key issues that have plagued existing regression-based myoelectric control systems. These findings are supported by two novel metrics, namely action interference and simultaneity gain, which show that adding simultaneity often increases instability in the form of undesired and uncontrollable simultaneous motions. These findings demonstrate the viability of CIIL in an unconstrained, velocity-controlled environment for regression-based myoelectric control, and highlight the importance of capturing user behaviours when training regression-based myoelectric control systems. Source code is available on https://github.com/cbmorrell/adaptive-regression.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".