Selecting Muscles for Detection of Upper-Limb Compensatory Movements Using s-EMG Sensors
Bibliographic record
Abstract
Patients with upper-limb injuries often use compensatory movements to overcome limitations in range of motion, which can lead to additional injury if not corrected early within a rehabilitation program. Although automatic detection of compensatory movements has been studied in the literature, the impact of sensor locations on detection performance has not been previously explored. To investigate how sensor locations affect the ability to automatically detect compensatory movements of the upper limb, sixteen surface electromyography sensors were placed on key muscles involved in these movements. Thirty-one healthy participants performed a door-opening task in three conditions: without elbow restrictions (healthy pattern), and two conditions with limited elbow range of motion (60° of flexion-full flexion and 30°–80° of flexion to simulate injury). Statistical analyses identified sensor locations with significant differences between the conditions. Support vector machine classifiers demonstrated notably higher performance using data from six sensors on the middle deltoid, the upper trapezius, the latissimus dorsi, the external obliques, and the erector abdominis. This study highlights the importance of thoughtful muscle selection for effective automatic detection and correction of upper-limb compensatory movements, which is crucial for a wearable mechatronic device to be effective in improving the movement quality of patients.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.000 |
| 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".