EMG-based body–machine interface for targeted trunk muscle activation
Why this work is in the frame
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Bibliographic record
Abstract
Deficits in trunk control, commonly observed in individuals with neurological conditions, can significantly impair balance, posture, and functional movements. Body-machine interfaces (BoMIs) are promising tools for trunk rehabilitation, as they can provide real-time feedback on user movements and muscle activity, allowing for continuous monitoring and guidance during motor control training. However, research on BoMIs for trunk rehabilitation is limited, and current methods often lack precision in addressing trunk muscle deficits. This work introduces a BoMI that combines trunk electromyography (EMG) and motion data to selectively modulate trunk muscle activity during motor control tasks. The system utilizes machine learning to generate personalized trunk motion trajectories based on predefined EMG profiles. Each trajectory is displayed on a screen as a moving target, which users must follow by controlling the BoMI with their trunk movements. We hypothesize that by visually guiding users to track these generated trajectories, the BoMI could evoke the EMG patterns implicitly encoded within them. Tested with neurotypical individuals, the BoMI effectively elicited the desired trunk EMG profiles, achieving a mean similarity index of 0.82 ± 0.13, a correlation coefficient of 0.95 ± 0.03, and minimal timing mismatches. These results support the feasibility of using an EMG-based BoMI for precise trunk muscle training, which could potentially assist therapists in more efficiently monitoring and adjusting patients’ muscle engagement during interventions. Future work will focus on developing a control framework to dynamically adapt task difficulty to users’ needs, expanding the approach to include additional trunk muscles, and evaluating its translation to individuals with trunk muscle impairments.
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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 | 0.001 | 0.001 |
| 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 it